{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "277137d1-e28d-4fad-b9cf-996f4fba5e6f",
   "metadata": {},
   "source": [
    "# 前置说明"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fb031fd-2528-4fe4-9bbc-b77fb81f6f15",
   "metadata": {},
   "source": [
    "## 环境说明"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b7fc0e-ee80-44e4-8306-3388af5b3394",
   "metadata": {},
   "source": [
    "conda版本：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2021.11-Windows-x86_64.exe\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45cf17da-db34-4cab-ac8f-27fba32b1fe1",
   "metadata": {},
   "source": [
    "python核心包：\n",
    "- python-docx == 0.8.11"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0600cfd0-09aa-4aaf-9bcb-d0ee2e0aa1ef",
   "metadata": {},
   "source": [
    "## 模板说明"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f01a3c27-d8c7-46b2-b878-5cd757c2db6b",
   "metadata": {},
   "source": [
    "目的：学习模板，核对数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac2ac85b-0e37-43fd-99cb-8840e0c259b2",
   "metadata": {},
   "source": [
    "[【国君食品 | 年报点评】贵州茅台：品质突出，价值突显—2021年年报点评](https://mp.weixin.qq.com/s?__biz=MzA3NTM1MTQyMg==&mid=2652905961&idx=1&sn=e81ea78e4dc9dc4138efc9216875392b&chksm=84a555f8b3d2dcee650f9f4887f115004f1f246abe26548db74cf28c1a016da7f1938cba6fb6#rd&cid=2663336533939694)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d094bbf5-de0f-4274-b68c-650e75632318",
   "metadata": {},
   "source": [
    "# 数据获取"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ce48f8-9668-4bb7-94ea-f2fad3a0fc87",
   "metadata": {},
   "source": [
    "## 爬取数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa7b54b7-6728-4326-869d-ecfe429e95bf",
   "metadata": {},
   "source": [
    "- 参考B站T7视频，这里获取6年的数据\n",
    "- 强调爬虫可能失效，请大家根据实际情况修改代码，也欢迎热心网友弹幕更正：“202X-XX-XX 爬虫代码失效，我已将调整代码放至评论区”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b757e2ab-21c3-41bd-80dc-f4fbc84b5721",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e00d1373-8397-439a-aee2-b1d739c4fe76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 结合网址去查看\n",
    "zcfz_name2code_dict = {\n",
    "    '股票代码': 'SECUCODE'\n",
    "    ,'股票简称': 'SECURITY_NAME_ABBR'\n",
    "    ,'行业名称': 'INDUSTRY_NAME'\n",
    "    ,'公告日期': 'REPORT_DATE'\n",
    "    ,'总资产': 'TOTAL_ASSETS'\n",
    "    ,'固定资产': 'FIXED_ASSET'\n",
    "    ,'货币资金': 'MONETARYFUNDS'\n",
    "    ,'应收账款': 'ACCOUNTS_RECE'\n",
    "    ,'存货': 'INVENTORY'\n",
    "    ,'总负债': 'TOTAL_LIABILITIES'\n",
    "    ,'应付账款': 'ACCOUNTS_PAYABLE'\n",
    "    ,'预收账款': 'ADVANCE_RECEIVABLES'\n",
    "    ,'所有者权益': 'TOTAL_EQUITY'\n",
    "}\n",
    "zcfz_code2name_dict = {v:k for k,v in zcfz_name2code_dict.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "011907fd-9b45-40cc-a1f3-87f14b2757ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 结合网址去查看\n",
    "lrb_name2code_dict = {\n",
    "    '股票代码': 'SECUCODE'\n",
    "    ,'股票简称': 'SECURITY_NAME_ABBR'\n",
    "    ,'行业名称': 'INDUSTRY_NAME'\n",
    "    ,'公告日期': 'REPORT_DATE'\n",
    "    ,'营业收入': 'TOTAL_OPERATE_INCOME'\n",
    "    ,'营业成本': 'OPERATE_COST'\n",
    "    ,'销售费用': 'SALE_EXPENSE'\n",
    "    ,'管理费用': 'MANAGE_EXPENSE'\n",
    "    ,'财务费用': 'FINANCE_EXPENSE'\n",
    "    ,'营业总支出': 'TOTAL_OPERATE_COST'\n",
    "    ,'营业利润': 'OPERATE_PROFIT'\n",
    "    ,'利润总额': 'TOTAL_PROFIT'\n",
    "    ,'所得税费用': 'INCOME_TAX'\n",
    "    ,'净利润': 'OPERATE_INCOME'\n",
    "}\n",
    "lrb_code2name_dict = {v:k for k,v in lrb_name2code_dict.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b4001d3-5ab9-4960-978b-499e0bae328f",
   "metadata": {},
   "outputs": [],
   "source": [
    "report_date_list = ['2016-12-31', '2017-12-31', '2018-12-31', '2019-12-31', '2020-12-31', '2021-12-31']\n",
    "pageSize = 500\n",
    "pageNumber_list = range(20) # 不会超过20了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8491494d-257b-4354-91a3-bf6ddc4d92b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_zcfz_df_v2(api, code2name_dict):\n",
    "    res = json.loads(requests.get(api).text)['result']\n",
    "    if res==None:  # 如果为空就不解析了\n",
    "        return pd.DataFrame([])\n",
    "    zcfz_df = pd.DataFrame(res['data'])\n",
    "    zcfz_df = zcfz_df.loc[:, code2name_dict]\n",
    "    zcfz_df = zcfz_df.rename(columns=code2name_dict)\n",
    "    return zcfz_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7368ff8c-6454-4cd9-811d-169752399cbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lrb_df_v2(api, code2name_dict):\n",
    "    res = json.loads(requests.get(api).text)['result']\n",
    "    if res==None:  # 如果为空就不解析了\n",
    "        return pd.DataFrame([])        \n",
    "    lrb_df = pd.DataFrame(res['data'])\n",
    "    lrb_df = lrb_df.loc[:, code2name_dict]\n",
    "    lrb_df = lrb_df.rename(columns=code2name_dict)\n",
    "    return lrb_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6217594e-9575-4115-b840-5c18ebcd10d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_zcfz_df = pd.DataFrame([])\n",
    "for REPORT_DATE in report_date_list:\n",
    "    print('开始获取', REPORT_DATE)\n",
    "    for pageNumber in pageNumber_list:\n",
    "            zcfz_api = f'https://datacenter-web.eastmoney.com/api/data/v1/get?&sortColumns=NOTICE_DATE%2CSECURITY_CODE&sortTypes=-1%2C-1&'\\\n",
    "    f'pageSize={pageSize}&pageNumber={pageNumber}&reportName=RPT_DMSK_FN_BALANCE&columns=ALL&filter=(SECURITY_TYPE_CODE+in+(\"058001001\"%2C\"058001008\"))(TRADE_MARKET_CODE!%3D\"069001017\")'\\\n",
    "    f'(REPORT_DATE%3D%27{REPORT_DATE}%27)'\n",
    "            tmp_zcfz_df = get_zcfz_df_v2(zcfz_api, code2name_dict=zcfz_code2name_dict)\n",
    "            if tmp_zcfz_df.shape[0] == 0:\n",
    "                break\n",
    "            else:\n",
    "                if total_zcfz_df.shape[0] == 0:\n",
    "                    total_zcfz_df = tmp_zcfz_df\n",
    "                else:\n",
    "                    total_zcfz_df = pd.concat([total_zcfz_df, tmp_zcfz_df], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4edd5ade-e122-4c66-a7a7-8d0fc9c511a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_zcfz_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "894fafdf-7420-4c3b-9c73-526dc672faf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_lrb_df = pd.DataFrame([])\n",
    "for REPORT_DATE in report_date_list:\n",
    "    print('开始获取', REPORT_DATE)\n",
    "    for pageNumber in pageNumber_list:\n",
    "            lrb_api = f'https://datacenter-web.eastmoney.com/api/data/v1/get?sortColumns=NOTICE_DATE%2CSECURITY_CODE&sortTypes=-1%2C-1&'\\\n",
    "f'pageSize={pageSize}&pageNumber={pageNumber}&reportName=RPT_DMSK_FN_INCOME&columns=ALL&filter=(SECURITY_TYPE_CODE+in+(%22058001001%22%2C%22058001008%22))(TRADE_MARKET_CODE!%3D%22069001017%22)'\\\n",
    "f'(REPORT_DATE%3D%27{REPORT_DATE}%27)'\n",
    "            tmp_lrb_df = get_lrb_df_v2(lrb_api, code2name_dict=lrb_code2name_dict)\n",
    "            if tmp_lrb_df.shape[0] == 0:\n",
    "                break\n",
    "            else:\n",
    "                if tmp_lrb_df.shape[0] == 0:\n",
    "                    total_lrb_df = tmp_lrb_df\n",
    "                else:\n",
    "                    total_lrb_df = pd.concat([total_lrb_df, tmp_lrb_df], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88624715-09bc-4153-85a0-b8de37e71fa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_lrb_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "454f29c2-aa10-475a-8090-e8e140327f1b",
   "metadata": {},
   "source": [
    "## 优化数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3486ed07-0764-421c-af0b-433299d630ee",
   "metadata": {},
   "source": [
    "### 去重拼接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b3d2e6c-2e95-4d29-9fe6-c7041a5e7bee",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_zcfz_dropdu_df = total_zcfz_df.drop_duplicates(subset=['公告日期', '股票代码'])\n",
    "total_lrb_dropdu_df = total_lrb_df.drop_duplicates(subset=['公告日期', '股票代码'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a1863bf-9ece-4828-af6c-9d417a0bda2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_zcfz_df_index = total_zcfz_dropdu_df.set_index(['公告日期', '股票代码'])\n",
    "total_lrb_df_index = total_lrb_dropdu_df.set_index(['公告日期', '股票代码'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71a2464f-3ab4-4d45-ab2c-2f7f1fe288bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df = pd.concat([total_zcfz_df_index, total_lrb_df_index.iloc[:, 2:]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6f4bbe2-ea93-4f32-9dbf-3837db009154",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df.iloc[:2].T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb1d4618-8f41-4ecc-9323-5e9741244fd7",
   "metadata": {},
   "source": [
    "### 修复字段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "336305fc-660c-459e-ab93-27f162ef8cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df['净利润'] = total_df['利润总额'] - total_df['所得税费用']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0165d67d-1be1-4b2d-b54f-69d75bee07a4",
   "metadata": {},
   "source": [
    "### 衍生字段"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf609d5a-2049-484e-a070-343d0fa8f3bf",
   "metadata": {},
   "source": [
    "#### 杜邦分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dfead46f-e685-41e4-93f3-86faac0f02ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df['同期资产'] = total_df['总资产'].unstack('股票代码').shift(1).stack('股票代码')\n",
    "total_df['同期权益'] = total_df['所有者权益'].unstack('股票代码').shift(1).stack('股票代码')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "123fa110-9aac-4054-b5ef-ca14508b7e1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df['净资产收益率'] = total_df['净利润'] / (total_df['所有者权益'] + total_df['同期权益']) * 2\n",
    "total_df['利润率'] = total_df['净利润'] / total_df['营业收入']\n",
    "total_df['资产周转率'] = total_df['营业收入'] / (total_df['总资产'] + total_df['同期资产']) * 2\n",
    "total_df['权益乘数'] = total_df['总资产'] / total_df['所有者权益']\n",
    "\n",
    "total_df['资产负债率'] = total_df['总负债'] / total_df['总资产']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b46a1fb-940a-45a6-a8ca-de73db773c45",
   "metadata": {},
   "source": [
    "#### 毛利率和三费"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d02eaac5-5a24-4dad-a3ce-c729146d3d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df['毛利率'] = 1 - total_df['营业成本'] / total_df['营业收入']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6b9e5cd9-de02-4620-98d8-8549cc32baf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df['销售费用率'] = total_df['销售费用'] / total_df['营业收入']\n",
    "total_df['管理费用率'] = total_df['管理费用'] / total_df['营业收入']\n",
    "total_df['财务费用率'] = total_df['财务费用'] / total_df['营业收入']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fc838fd-bb42-40a5-b620-acf2880b77e6",
   "metadata": {},
   "source": [
    "#### EBIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "59e747a1-de35-4a8f-932a-a9168b8caae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df['息税前利润'] = total_df['利润总额'] + total_df['财务费用']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "574c6b95-50a5-42a9-98e8-e47a2a03d4d5",
   "metadata": {},
   "source": [
    "## 数据导出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "0c3ad44e-cc6c-487c-b567-be1f68038016",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df.to_csv('2023-01-08 加工导出.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "070f2ed3-26b7-441d-b3eb-05797538ff29",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 素材准备"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98784811-c27e-45b1-b2b5-7aa8ce17c1b2",
   "metadata": {},
   "source": [
    "## 文字准备"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c05b22e2-38c9-491c-b92e-581539c93b94",
   "metadata": {},
   "source": [
    "pip install python-docx==0.8.11 -i https://pypi.douban.com/simple"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411454f3-0d9f-49da-8962-51d8525b14bf",
   "metadata": {},
   "source": [
    "官网参考：https://python-docx.readthedocs.io/en/latest/#contributor-guide"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baf3505d-d26c-4d6d-974e-f474c0ab8dea",
   "metadata": {},
   "source": [
    "### 官网demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1a16e692-b353-4bfb-abc8-0cbc0ef215a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from docx import Document\n",
    "from docx.shared import Inches\n",
    "\n",
    "document = Document()\n",
    "\n",
    "document.add_heading('Document Title', 0)\n",
    "\n",
    "p = document.add_paragraph('A plain paragraph having some ')\n",
    "p.add_run('bold').bold = True\n",
    "p.add_run(' and some ')\n",
    "p.add_run('italic.').italic = True\n",
    "\n",
    "document.add_heading('Heading, level 1', level=1)\n",
    "document.add_paragraph('Intense quote', style='Intense Quote')\n",
    "\n",
    "document.add_paragraph(\n",
    "    'first item in unordered list', style='List Bullet'\n",
    ")\n",
    "document.add_paragraph(\n",
    "    'first item in ordered list', style='List Number'\n",
    ")\n",
    "\n",
    "# document.add_picture('monty-truth.png', width=Inches(1.25))\n",
    "\n",
    "records = (\n",
    "    (3, '101', 'Spam'),\n",
    "    (7, '422', 'Eggs'),\n",
    "    (4, '631', 'Spam, spam, eggs, and spam')\n",
    ")\n",
    "\n",
    "table = document.add_table(rows=1, cols=3)\n",
    "hdr_cells = table.rows[0].cells\n",
    "hdr_cells[0].text = 'Qty'\n",
    "hdr_cells[1].text = 'Id'\n",
    "hdr_cells[2].text = 'Desc'\n",
    "for qty, id, desc in records:\n",
    "    row_cells = table.add_row().cells\n",
    "    row_cells[0].text = str(qty)\n",
    "    row_cells[1].text = id\n",
    "    row_cells[2].text = desc\n",
    "\n",
    "document.add_page_break()\n",
    "\n",
    "document.save('demo.docx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6faff4ff-cdfb-4259-94e7-783cc30630ac",
   "metadata": {},
   "source": [
    "### 获取指定股票数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b09f2d7-1e52-470d-8177-f95ffb71447e",
   "metadata": {},
   "source": [
    "#### 股票索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5b391aa4-0422-4127-bc08-dbdbb8dcc5a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设定参数\n",
    "stock_id, report_year = '600519.SH', '2021'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a0b360b6-18bf-4c89-b687-e8c73a31cfd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_df = total_df.swaplevel().loc[stock_id].sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a0aeff92-0f86-4fb9-ad4c-fa3cfc8430a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票简称</th>\n",
       "      <th>行业名称</th>\n",
       "      <th>总资产</th>\n",
       "      <th>固定资产</th>\n",
       "      <th>货币资金</th>\n",
       "      <th>应收账款</th>\n",
       "      <th>存货</th>\n",
       "      <th>总负债</th>\n",
       "      <th>应付账款</th>\n",
       "      <th>预收账款</th>\n",
       "      <th>...</th>\n",
       "      <th>同期权益</th>\n",
       "      <th>净资产收益率</th>\n",
       "      <th>利润率</th>\n",
       "      <th>资产周转率</th>\n",
       "      <th>权益乘数</th>\n",
       "      <th>资产负债率</th>\n",
       "      <th>毛利率</th>\n",
       "      <th>销售费用率</th>\n",
       "      <th>管理费用率</th>\n",
       "      <th>财务费用率</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>公告日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-31 00:00:00</th>\n",
       "      <td>贵州茅台</td>\n",
       "      <td>酿酒行业</td>\n",
       "      <td>1.129345e+11</td>\n",
       "      <td>1.445318e+10</td>\n",
       "      <td>6.685496e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.062225e+10</td>\n",
       "      <td>3.703600e+10</td>\n",
       "      <td>1.040608e+09</td>\n",
       "      <td>1.754108e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.446535</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.487967</td>\n",
       "      <td>0.327942</td>\n",
       "      <td>0.915077</td>\n",
       "      <td>0.041864</td>\n",
       "      <td>0.104275</td>\n",
       "      <td>-0.000826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31 00:00:00</th>\n",
       "      <td>贵州茅台</td>\n",
       "      <td>酿酒行业</td>\n",
       "      <td>1.346101e+11</td>\n",
       "      <td>1.524410e+10</td>\n",
       "      <td>8.786887e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.205748e+10</td>\n",
       "      <td>3.859049e+10</td>\n",
       "      <td>9.920559e+08</td>\n",
       "      <td>1.442911e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>7.589854e+10</td>\n",
       "      <td>0.337445</td>\n",
       "      <td>0.475026</td>\n",
       "      <td>0.493347</td>\n",
       "      <td>1.401902</td>\n",
       "      <td>0.286683</td>\n",
       "      <td>0.902716</td>\n",
       "      <td>0.048902</td>\n",
       "      <td>0.076999</td>\n",
       "      <td>-0.000913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31 00:00:00</th>\n",
       "      <td>贵州茅台</td>\n",
       "      <td>酿酒行业</td>\n",
       "      <td>1.598467e+11</td>\n",
       "      <td>1.524856e+10</td>\n",
       "      <td>1.120748e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.350695e+10</td>\n",
       "      <td>4.243819e+10</td>\n",
       "      <td>1.178296e+09</td>\n",
       "      <td>1.357652e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>9.601963e+10</td>\n",
       "      <td>0.354495</td>\n",
       "      <td>0.490025</td>\n",
       "      <td>0.524351</td>\n",
       "      <td>1.361458</td>\n",
       "      <td>0.265493</td>\n",
       "      <td>0.915506</td>\n",
       "      <td>0.033317</td>\n",
       "      <td>0.068989</td>\n",
       "      <td>-0.000046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 00:00:00</th>\n",
       "      <td>贵州茅台</td>\n",
       "      <td>酿酒行业</td>\n",
       "      <td>1.830424e+11</td>\n",
       "      <td>1.514418e+10</td>\n",
       "      <td>1.325182e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.528492e+10</td>\n",
       "      <td>4.116599e+10</td>\n",
       "      <td>1.513677e+09</td>\n",
       "      <td>1.374033e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>1.174085e+11</td>\n",
       "      <td>0.339164</td>\n",
       "      <td>0.494855</td>\n",
       "      <td>0.518269</td>\n",
       "      <td>1.290154</td>\n",
       "      <td>0.224899</td>\n",
       "      <td>0.916380</td>\n",
       "      <td>0.036903</td>\n",
       "      <td>0.069417</td>\n",
       "      <td>0.000084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31 00:00:00</th>\n",
       "      <td>贵州茅台</td>\n",
       "      <td>酿酒行业</td>\n",
       "      <td>2.133958e+11</td>\n",
       "      <td>1.622508e+10</td>\n",
       "      <td>3.609109e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.886909e+10</td>\n",
       "      <td>4.567513e+10</td>\n",
       "      <td>1.342268e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.418764e+11</td>\n",
       "      <td>0.319921</td>\n",
       "      <td>0.505375</td>\n",
       "      <td>0.494368</td>\n",
       "      <td>1.272329</td>\n",
       "      <td>0.214039</td>\n",
       "      <td>0.916790</td>\n",
       "      <td>0.025999</td>\n",
       "      <td>0.069289</td>\n",
       "      <td>-0.002394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31 00:00:00</th>\n",
       "      <td>贵州茅台</td>\n",
       "      <td>酿酒行业</td>\n",
       "      <td>2.551682e+11</td>\n",
       "      <td>1.747217e+10</td>\n",
       "      <td>5.181024e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.339437e+10</td>\n",
       "      <td>5.821069e+10</td>\n",
       "      <td>2.009832e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.677207e+11</td>\n",
       "      <td>0.305587</td>\n",
       "      <td>0.509029</td>\n",
       "      <td>0.467233</td>\n",
       "      <td>1.295549</td>\n",
       "      <td>0.228127</td>\n",
       "      <td>0.917933</td>\n",
       "      <td>0.025007</td>\n",
       "      <td>0.077197</td>\n",
       "      <td>-0.008537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     股票简称  行业名称           总资产          固定资产          货币资金  \\\n",
       "公告日期                                                                        \n",
       "2016-12-31 00:00:00  贵州茅台  酿酒行业  1.129345e+11  1.445318e+10  6.685496e+10   \n",
       "2017-12-31 00:00:00  贵州茅台  酿酒行业  1.346101e+11  1.524410e+10  8.786887e+10   \n",
       "2018-12-31 00:00:00  贵州茅台  酿酒行业  1.598467e+11  1.524856e+10  1.120748e+11   \n",
       "2019-12-31 00:00:00  贵州茅台  酿酒行业  1.830424e+11  1.514418e+10  1.325182e+10   \n",
       "2020-12-31 00:00:00  贵州茅台  酿酒行业  2.133958e+11  1.622508e+10  3.609109e+10   \n",
       "2021-12-31 00:00:00  贵州茅台  酿酒行业  2.551682e+11  1.747217e+10  5.181024e+10   \n",
       "\n",
       "                     应收账款            存货           总负债          应付账款  \\\n",
       "公告日期                                                                  \n",
       "2016-12-31 00:00:00   NaN  2.062225e+10  3.703600e+10  1.040608e+09   \n",
       "2017-12-31 00:00:00   NaN  2.205748e+10  3.859049e+10  9.920559e+08   \n",
       "2018-12-31 00:00:00   NaN  2.350695e+10  4.243819e+10  1.178296e+09   \n",
       "2019-12-31 00:00:00   NaN  2.528492e+10  4.116599e+10  1.513677e+09   \n",
       "2020-12-31 00:00:00   NaN  2.886909e+10  4.567513e+10  1.342268e+09   \n",
       "2021-12-31 00:00:00   NaN  3.339437e+10  5.821069e+10  2.009832e+09   \n",
       "\n",
       "                             预收账款  ...          同期权益    净资产收益率       利润率  \\\n",
       "公告日期                               ...                                     \n",
       "2016-12-31 00:00:00  1.754108e+10  ...           NaN       NaN  0.446535   \n",
       "2017-12-31 00:00:00  1.442911e+10  ...  7.589854e+10  0.337445  0.475026   \n",
       "2018-12-31 00:00:00  1.357652e+10  ...  9.601963e+10  0.354495  0.490025   \n",
       "2019-12-31 00:00:00  1.374033e+10  ...  1.174085e+11  0.339164  0.494855   \n",
       "2020-12-31 00:00:00           NaN  ...  1.418764e+11  0.319921  0.505375   \n",
       "2021-12-31 00:00:00           NaN  ...  1.677207e+11  0.305587  0.509029   \n",
       "\n",
       "                        资产周转率      权益乘数     资产负债率       毛利率     销售费用率  \\\n",
       "公告日期                                                                    \n",
       "2016-12-31 00:00:00       NaN  1.487967  0.327942  0.915077  0.041864   \n",
       "2017-12-31 00:00:00  0.493347  1.401902  0.286683  0.902716  0.048902   \n",
       "2018-12-31 00:00:00  0.524351  1.361458  0.265493  0.915506  0.033317   \n",
       "2019-12-31 00:00:00  0.518269  1.290154  0.224899  0.916380  0.036903   \n",
       "2020-12-31 00:00:00  0.494368  1.272329  0.214039  0.916790  0.025999   \n",
       "2021-12-31 00:00:00  0.467233  1.295549  0.228127  0.917933  0.025007   \n",
       "\n",
       "                        管理费用率     财务费用率  \n",
       "公告日期                                     \n",
       "2016-12-31 00:00:00  0.104275 -0.000826  \n",
       "2017-12-31 00:00:00  0.076999 -0.000913  \n",
       "2018-12-31 00:00:00  0.068989 -0.000046  \n",
       "2019-12-31 00:00:00  0.069417  0.000084  \n",
       "2020-12-31 00:00:00  0.069289 -0.002394  \n",
       "2021-12-31 00:00:00  0.077197 -0.008537  \n",
       "\n",
       "[6 rows x 32 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4236cefd-5016-44a3-a0d1-457031041b33",
   "metadata": {},
   "source": [
    "#### 股票元素"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af701c72-817a-4e7b-8995-e505635ef9c2",
   "metadata": {},
   "source": [
    "获取股票名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a0356ffc-0011-4e16-8574-b52111478dd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'贵州茅台'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_df['股票简称'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7037f330-a788-4319-85da-5dec3957b527",
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_data_row = stock_df.shape[0]\n",
    "if stock_data_row > 0:\n",
    "    stock_name = stock_df['股票简称'][0]\n",
    "else:  # raise是主动抛出异常\n",
    "    raise Exception('不存在该股票的数据')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48f8e2b9-fb9c-4a83-b26a-07929d64e08f",
   "metadata": {},
   "source": [
    "获取时间索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "92628eee-02ee-44ee-958a-9c229c036312",
   "metadata": {},
   "outputs": [],
   "source": [
    "report_date = f'{report_year}-12-31 00:00:00'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "be0f4c46-706c-459d-9357-34d45284cd07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2021-12-31 00:00:00'"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "04497065-b65d-4e1f-8b2e-3b269de77840",
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_df = stock_df.loc[:report_date, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5e79bfaf-2084-4cfd-a387-24e597ffe220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2016-12-31 00:00:00', '2017-12-31 00:00:00', '2018-12-31 00:00:00',\n",
       "       '2019-12-31 00:00:00', '2020-12-31 00:00:00', '2021-12-31 00:00:00'],\n",
       "      dtype='object', name='公告日期')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6efb853b-e9c7-4a6b-9c86-573ea015d8b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2016-12-31 00:00:00'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 一定要选到元素看看，防止是其他类型\n",
    "stock_df.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "804a952e-c84a-4f41-886c-0dd59f4e4f39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['股票简称', '行业名称', '总资产', '固定资产', '货币资金', '应收账款', '存货', '总负债', '应付账款',\n",
       "       '预收账款', '所有者权益', '营业收入', '营业成本', '销售费用', '管理费用', '财务费用', '营业总支出',\n",
       "       '营业利润', '利润总额', '所得税费用', '净利润', '同期资产', '同期权益', '净资产收益率', '利润率',\n",
       "       '资产周转率', '权益乘数', '资产负债率'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0544a18b-eb23-40f8-8e91-376a6af7cdd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255168195159.9"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这样就可以随便获取数据了\n",
    "stock_df.loc[report_date, '总资产']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f21d661-1cf7-4c25-8ab2-0b745fcf3b57",
   "metadata": {},
   "source": [
    "获取股票行业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c723e4b6-53e7-4743-a9f7-507441ef2d84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'酿酒行业'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_industry = stock_df['行业名称'][0]\n",
    "stock_industry"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "209db539-292a-476d-b833-d9be0fc90305",
   "metadata": {},
   "source": [
    "### 文字规划"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34c589c0-fe9b-4304-9091-a55ce37148d7",
   "metadata": {},
   "source": [
    "- 1 公司收益很不错：营业收入、净利润（当前、同比）\n",
    "- 2 公司降本提效了：净利率、毛利率提升，三费下降（当前、同比）\n",
    "- 3 公司财务状况好：净资产收益率提升，净利率、资产周转率、权益乘数状况良好\n",
    "- 4 公司在行业表现好：从一些好的指标中循环，找到排名前3的指标，有就输出结果（报告都出来后才能分析）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a4bada1-07ff-4cf1-a469-7cabea1736d8",
   "metadata": {},
   "source": [
    "其他数据对比渠道：https://emweb.securities.eastmoney.com/PC_HSF10/FinanceAnalysis/Index?type=web&code=SH600519#dbfx-0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d00be282-486d-4ef9-a7ea-2c87565b626f",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### P1-公司收益很不错"
   ]
  },
  {
   "cell_type": "raw",
   "id": "654c1536-d761-4534-a2ed-e389c5f1aa20",
   "metadata": {},
   "source": [
    "2021年度实现营业总收入 1094.64亿元（含利息收入32.74亿元）、同比+11.71%，其中酒类收入1060.59亿元、同比+11.85%，归母净利524.6亿元、同比+12.34%，拟10派216.75元(含税)，分红率51.9%。Q4单季度营收315.48亿，同比+13.9%；归母净利151.94亿，同比+18.1%，Q4环比加速明显。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ced79ad-1ab5-4dcf-84a7-55e752fe303c",
   "metadata": {},
   "source": [
    "归母净利润=净利润-少数股东收益  \n",
    "这里原始数据没有，就先用净利润替代"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4b45e18-c7fe-4d97-b037-c97bc05a370b",
   "metadata": {},
   "source": [
    "##### 计算指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "71765246-486d-4aca-9cd7-71d119393705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "公告日期\n",
       "2016-12-31 00:00:00    4.015508e+10\n",
       "2017-12-31 00:00:00    6.106276e+10\n",
       "2018-12-31 00:00:00    7.719938e+10\n",
       "2019-12-31 00:00:00    8.885434e+10\n",
       "2020-12-31 00:00:00    9.799324e+10\n",
       "2021-12-31 00:00:00    1.094643e+11\n",
       "Name: 营业收入, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_df['营业收入']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9ad86b64-31b9-4f01-8724-c34d50144c2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "revenue = stock_df['营业收入'][-1]\n",
    "last_revenue = stock_df['营业收入'][-2]\n",
    "net_income = stock_df['净利润'][-1]\n",
    "last_income = stock_df['净利润'][-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "57076386-dd62-41fd-a4e5-65dfd9b7c869",
   "metadata": {},
   "outputs": [],
   "source": [
    "revenue_rate = revenue / last_revenue - 1\n",
    "net_income_rate = net_income / last_income - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c262f03-59e9-4aba-9c13-af054fa252ac",
   "metadata": {},
   "source": [
    "##### 拼接文字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "afb8f376-45b4-4237-90c1-f46283141cee",
   "metadata": {},
   "outputs": [],
   "source": [
    "revenue_flag = '增长' if revenue_rate > 0 else '下降'\n",
    "net_income_flag = '增长' if net_income_rate > 0 else '下降'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "63358d69-d7aa-430c-9515-79d83577a96a",
   "metadata": {},
   "outputs": [],
   "source": [
    "p1_text_1 = f'{stock_name}({stock_id})全年实现营业总收入{\"%.2f\" % (revenue/1e8)}亿元，同比{revenue_flag}{\"%.2f\" % (revenue_rate*100)}%；'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "68a3cdf5-a623-4d97-acdf-fb9af172c1ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'贵州茅台(600519.SH)全年实现营业总收入1094.64亿元，同比增长11.71%；'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1_text_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "88928835-5c15-41a8-b607-5c4e90b728a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "p1_text_2 = f'实现净利润{\"%.2f\" %(net_income/1e8)}亿元，同比{net_income_flag}{\"%.2f\" % (net_income_rate*100)}%。'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9d62fd2c-654d-4977-84f9-3faf456b6866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'实现净利润557.21亿元，同比增长12.51%。'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1_text_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "2ecb48f4-7789-4962-855a-20a9f381bd58",
   "metadata": {},
   "outputs": [],
   "source": [
    "p1_text = p1_text_1 + p1_text_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9f6533ea-4ca1-4406-a35d-62f438f5d887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'贵州茅台(600519.SH)全年实现营业总收入1094.64亿元，同比增长11.71%；实现净利润557.21亿元，同比增长12.51%。'"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1_text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2fe89dc-9b3c-4962-9630-c8d96f3015ef",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### P2-公司降本提效了"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c81be02a-5e58-451d-9c3e-d34ab5edab37",
   "metadata": {},
   "source": [
    "Q4毛利率、净利率提升，预收维持高位。Q4毛利率同比+0.75pct至92.4%，预计主要系渠道结构优化、直销占比提升拉动，税金及附加同比-1pct至15%，Q4销售费用率同比-0.2pct至2.5%，管理费用率同比+1.5pct至9.4%，预计主要系人工成本上升，最终Q4净利率同比+1.8pct至51.2%。Q4单季度销售收现398.9亿元、同比+11.8%，与收入增速匹配，2021年末预收款（合同负债+其他流动负债）142.54亿元，环比+39.9亿元。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6a6c8ce-61fc-428f-a6b6-b118251aac84",
   "metadata": {},
   "source": [
    "##### 计算指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4e98b4a0-c647-4b76-a9cf-d1f69f17250c",
   "metadata": {},
   "outputs": [],
   "source": [
    "net_rate = stock_df['利润率'][-1]\n",
    "last_net_rate = stock_df['利润率'][-2]\n",
    "net_rate_inc = net_rate - last_net_rate\n",
    "\n",
    "gross_rate = stock_df['毛利率'][-1]\n",
    "last_gross_rate = stock_df['毛利率'][-2]\n",
    "gross_rate_inc = gross_rate - last_gross_rate\n",
    "\n",
    "sale_rate = stock_df['销售费用率'][-1]\n",
    "last_sale_rate = stock_df['销售费用率'][-2]\n",
    "sale_rate_inc = sale_rate - last_sale_rate\n",
    "\n",
    "admin_rate = stock_df['管理费用率'][-1]\n",
    "last_admin_rate = stock_df['管理费用率'][-2]\n",
    "admin_rate_inc = admin_rate - last_admin_rate\n",
    "\n",
    "finance_rate = stock_df['财务费用率'][-1]\n",
    "last_finance_rate = stock_df['财务费用率'][-2]\n",
    "finance_rate_inc = finance_rate - last_finance_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee25d675-004b-4fcd-aabb-bff2d60dba15",
   "metadata": {},
   "source": [
    "##### 拼接文字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "3e9c6c5f-3b3b-4e71-bd03-8a8047b5a9e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "net_rate_flag = '增长+' if net_rate_inc > 0 else '下降'\n",
    "gross_rate_flag = '增长+' if gross_rate_inc > 0 else '下降'\n",
    "sale_rate_flag = '增长+' if sale_rate_inc > 0 else '下降'\n",
    "admin_rate_flag = '增长+' if admin_rate_inc > 0 else '下降'\n",
    "finance_rate_flag = '增长+' if finance_rate_inc > 0 else '下降'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ae3419cf-1da0-4f6d-8ac4-ec384e43418a",
   "metadata": {},
   "outputs": [],
   "source": [
    "p2_text_1 = f'净利率同期{net_rate_flag}{\"%.2f\" % (net_rate_inc*100)}pct至{\"%.2f\" % (net_rate*100)}%，'\n",
    "p2_text_2 = f'毛利率同期{gross_rate_flag}{\"%.2f\" % (gross_rate_inc*100)}pct至{\"%.2f\" % (gross_rate*100)}%。'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "52b561f9-d043-4d59-a11a-d487c62f77e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "p2_text_3 = f'费用率方面，销售费用率同期{sale_rate_flag}{\"%.2f\" % (sale_rate_inc*100)}pct至{\"%.2f\" % (sale_rate*100)}%，'\n",
    "p2_text_4 = f'管理费用率同期{admin_rate_flag}{\"%.2f\" % (admin_rate_inc*100)}pct至{\"%.2f\" % (admin_rate*100)}%，'\n",
    "p2_text_5 = f'财务费用率同期{finance_rate_flag}{\"%.2f\" % (finance_rate_inc*100)}pct至{\"%.2f\" % (finance_rate*100)}%。'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f0fdb55c-ce41-4212-b549-7cab135a2722",
   "metadata": {},
   "outputs": [],
   "source": [
    "p2_text = p2_text_1 + p2_text_2 + p2_text_3 + p2_text_4 + p2_text_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "a9b7bbb5-6117-43a0-9f59-1e5e349956fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'净利率同期增长+0.37pct至50.90%，毛利率同期增长+0.11pct至91.79%。费用率方面，销售费用率同期下降-0.10pct至2.50%，管理费用率同期增长+0.79pct至7.72%，财务费用率同期下降-0.61pct至-0.85%。'"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2_text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b23f65f9-fabc-4634-a14e-e51674e46f75",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### P3-公司财务状况好"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb9b0443-414c-4fcf-840e-1d16c2ff5b5d",
   "metadata": {},
   "source": [
    "##### 计算指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "993fba0c-3b22-4dcd-b303-e218c25b2ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "roe = stock_df['净资产收益率'][-1]\n",
    "last_roe = stock_df['净资产收益率'][-2]\n",
    "roe_inc = roe - last_roe\n",
    "\n",
    "net_rate = stock_df['利润率'][-1]\n",
    "last_net_rate = stock_df['利润率'][-2]\n",
    "net_rate_inc = net_rate - last_net_rate\n",
    "\n",
    "asset_turnover_rate = stock_df['资产周转率'][-1]\n",
    "last_asset_turnover_rate = stock_df['资产周转率'][-2]\n",
    "asset_turnover_rate_inc = asset_turnover_rate - last_asset_turnover_rate\n",
    "\n",
    "debt_asset_rate = stock_df['资产负债率'][-1]\n",
    "last_debt_asset_rate = stock_df['资产负债率'][-2]\n",
    "debt_asset_rate_inc = debt_asset_rate - last_debt_asset_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6651128c-29b6-4d68-9c34-1b29f6968b81",
   "metadata": {},
   "source": [
    "##### 拼接文字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e26a1f35-ee3d-4071-8b30-89e3f2d56b60",
   "metadata": {},
   "outputs": [],
   "source": [
    "roe_flag = '增长+' if roe_inc > 0 else '下降'\n",
    "net_rate_flag = '增长+' if net_rate_inc > 0 else '下降'\n",
    "asset_turnover_rate_flag = '增长+' if asset_turnover_rate_inc > 0 else '下降'\n",
    "debt_asset_rate_flag = '增长+' if debt_asset_rate_inc > 0 else '下降'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "301250b0-90d5-4353-b28d-cbb4743bd2b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "p3_text_1 = f'ROE同期{roe_flag}{\"%.2f\" % (roe_inc*100)}pct至{\"%.2f\" % (roe*100)}%，根据杜邦分析法拆解，'\n",
    "p3_text_2 = f'净利率同期{net_rate_flag}{\"%.2f\" % (net_rate_inc*100)}pct至{\"%.2f\" % (net_rate*100)}%。'\n",
    "p3_text_3 = f'资产周转率同期{sale_rate_flag}{\"%.2f\" % (asset_turnover_rate_inc*100)}pct至{\"%.2f\" % (asset_turnover_rate*100)}%，'\n",
    "p3_text_4 = f'资产负债率同期{debt_asset_rate_flag}{\"%.2f\" % (debt_asset_rate_inc*100)}pct至{\"%.2f\" % (debt_asset_rate*100)}%。'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d7340374-1116-4e17-93a1-1e4ca7c4b87d",
   "metadata": {},
   "outputs": [],
   "source": [
    "p3_text = p3_text_1 + p3_text_2 + p3_text_3 + p3_text_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "68f7051c-936e-4010-a2b5-9c8878be2930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ROE同期下降-1.43pct至30.56%，根据杜邦分析法拆解，净利率同期增长+0.37pct至50.90%。资产周转率同期下降-2.71pct至46.72%，资产负债率同期增长+1.41pct至22.81%。'"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p3_text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81d33315-2824-4324-845c-93f72ccbc312",
   "metadata": {},
   "source": [
    "#### P4-公司在行业表现好"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab73b11-ba33-477a-b538-373c96327f5b",
   "metadata": {},
   "source": [
    "先选出一些指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "54ab2de3-4b11-416e-aaaa-75e2665a207f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设定重要性指标排序\n",
    "rank_index_list = ['净资产收益率', '利润率', '资产周转率', '资产负债率']\n",
    "# 1表示越大越好， 0表示越小越好\n",
    "index_direction_list = [1, 1, 1, 0]\n",
    "# 设定排名前几输出\n",
    "rank_threshold = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "4892cbea-d357-43a3-a389-ff00ee33c430",
   "metadata": {},
   "outputs": [],
   "source": [
    "report_year_df = total_df.loc[report_date]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "5af4dfb6-2fb9-40d6-9b52-851481281315",
   "metadata": {},
   "outputs": [],
   "source": [
    "industry_df = report_year_df[report_year_df['行业名称']==stock_industry]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "2ebc190f-72fd-4558-bd6d-8990048d38ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38, 32)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "industry_df.shape"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e1eeffef-a5b9-43c1-b03d-bacc7eaafb94",
   "metadata": {},
   "source": [
    "industry_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "b5696c98-cd09-4b3f-936a-eb18713b500c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "净资产收益率 1\n",
      "利润率 1\n",
      "资产周转率 1\n",
      "资产负债率 0\n"
     ]
    }
   ],
   "source": [
    "for index_name, direction in zip(rank_index_list, index_direction_list):\n",
    "    print(index_name, direction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "f440553c-eec0-4543-99bc-832fadbc478a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_ascending = False if direction else True\n",
    "tmp_df = industry_df.sort_values(by=index_name, ascending=tmp_ascending)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "3397d49a-2090-4a2c-a9c4-4d64cbb0063a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_df['股票简称'].tolist().index(stock_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "ee81b864-0e9f-44fb-9200-d48056431db2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先赋值，再更新\n",
    "p4_text = ''\n",
    "# TODO:拉链方法介绍\n",
    "for index_name, direction in zip(rank_index_list, index_direction_list):\n",
    "    # 注意这里方向是反过来的\n",
    "    tmp_ascending = False if direction else True\n",
    "    tmp_df = industry_df.sort_values(by=index_name, ascending=tmp_ascending)\n",
    "    tmp_rank = tmp_df['股票简称'].tolist().index(stock_name)\n",
    "    if tmp_rank + 1 <= rank_threshold:\n",
    "        tmp_value = tmp_df.loc[stock_id, index_name]\n",
    "        tmp_avg_value = tmp_df[index_name].mean()\n",
    "        direction_flag = '远超过' if direction else '远低于'\n",
    "        p4_text = f'公司的{index_name}表现良好，达到{\"%.2f\" % (tmp_value*100)}%，在行业内排名第{tmp_rank+1}，{direction_flag}行业平均的{\"%.2f\" % (tmp_avg_value*100)}%。'\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "a87e5805-4b6b-46af-bf12-26e085f0eb4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'公司的利润率表现良好，达到50.90%，在行业内排名第1，远超过行业平均的10.16%。'"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p4_text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8258c2b-2624-4f37-8f72-762fcd3008bd",
   "metadata": {},
   "source": [
    "### 函数封装"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae72ad7b-98ee-431e-b7cc-c1d3d6c4a92b",
   "metadata": {},
   "source": [
    "#### 解耦封装"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "88ec6fa3-13b7-42d8-8150-afc0d53f06ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stock_revenue_status(stock_name, stock_id, stock_df):\n",
    "    revenue = stock_df['营业收入'][-1]\n",
    "    last_revenue = stock_df['营业收入'][-2]\n",
    "    net_income = stock_df['净利润'][-1]\n",
    "    last_income = stock_df['净利润'][-2]\n",
    "    revenue_rate = revenue / last_revenue - 1\n",
    "    net_income_rate = net_income / last_income - 1\n",
    "    revenue_flag = '增长' if revenue_rate > 0 else '下降'\n",
    "    net_income_flag = '增长' if net_income_rate > 0 else '下降'\n",
    "    p1_text_1 = f'{stock_name}({stock_id})全年实现营业总收入{\"%.2f\" %(revenue/1e8)}亿元，同比{revenue_flag}{\"%.2f\" % (revenue_rate*100)}%；'\n",
    "    p1_text_2 = f'实现净利润{\"%.2f\" %(net_income/1e8)}亿元，同比{net_income_flag}{\"%.2f\" % (net_income_rate*100)}%。'\n",
    "    p1_text = p1_text_1 + p1_text_2\n",
    "    return p1_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "56cde9c8-ebd0-4dbb-9d5a-ec12d6a74357",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stock_efficiency_status(stock_name, stock_id, stock_df):\n",
    "    net_rate = stock_df['利润率'][-1]\n",
    "    last_net_rate = stock_df['利润率'][-2]\n",
    "    net_rate_inc = net_rate - last_net_rate\n",
    "\n",
    "    gross_rate = stock_df['毛利率'][-1]\n",
    "    last_gross_rate = stock_df['毛利率'][-2]\n",
    "    gross_rate_inc = gross_rate - last_gross_rate\n",
    "\n",
    "    sale_rate = stock_df['销售费用率'][-1]\n",
    "    last_sale_rate = stock_df['销售费用率'][-2]\n",
    "    sale_rate_inc = sale_rate - last_sale_rate\n",
    "\n",
    "    admin_rate = stock_df['管理费用率'][-1]\n",
    "    last_admin_rate = stock_df['管理费用率'][-2]\n",
    "    admin_rate_inc = admin_rate - last_admin_rate\n",
    "\n",
    "    finance_rate = stock_df['财务费用率'][-1]\n",
    "    last_finance_rate = stock_df['财务费用率'][-2]\n",
    "    finance_rate_inc = finance_rate - last_finance_rate\n",
    "\n",
    "    net_rate_flag = '增长' if net_rate_inc > 0 else '下降'\n",
    "    gross_rate_flag = '增长' if gross_rate_inc > 0 else '下降'\n",
    "    sale_rate_flag = '增长' if sale_rate_inc > 0 else '下降'\n",
    "    admin_rate_flag = '增长' if admin_rate_inc > 0 else '下降'\n",
    "    finance_rate_flag = '增长' if finance_rate_inc > 0 else '下降'\n",
    "\n",
    "    p2_text_1 = f'净利率同期{net_rate_flag}{\"%.2f\" % (net_rate_inc*100)}pct至{\"%.2f\" % (net_rate*100)}%，'\n",
    "    p2_text_2 = f'毛利率同期{gross_rate_flag}{\"%.2f\" % (gross_rate_inc*100)}pct至{\"%.2f\" % (gross_rate*100)}%。'\n",
    "    p2_text_3 = f'费用率方面，销售费用率同期{sale_rate_flag}{\"%.2f\" % (sale_rate_inc*100)}pct至{\"%.2f\" % (sale_rate*100)}%，'\n",
    "    p2_text_4 = f'管理费用率同期{admin_rate_flag}{\"%.2f\" % (admin_rate_inc*100)}pct至{\"%.2f\" % (admin_rate*100)}%，'\n",
    "    p2_text_5 = f'财务费用率同期{finance_rate_flag}{\"%.2f\" % (finance_rate_inc*100)}pct至{\"%.2f\" % (finance_rate*100)}%。'\n",
    "    p2_text = p2_text_1 + p2_text_2 + p2_text_3 + p2_text_4 + p2_text_5\n",
    "    return p2_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "cdbf6b93-2e4d-4438-9881-95be0d95ac6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stock_finance_status(stock_name, stock_id, stock_df):\n",
    "    roe = stock_df['净资产收益率'][-1]\n",
    "    last_roe = stock_df['净资产收益率'][-2]\n",
    "    roe_inc = roe - last_roe\n",
    "\n",
    "    net_rate = stock_df['利润率'][-1]\n",
    "    last_net_rate = stock_df['利润率'][-2]\n",
    "    net_rate_inc = net_rate - last_net_rate\n",
    "\n",
    "    asset_turnover_rate = stock_df['资产周转率'][-1]\n",
    "    last_asset_turnover_rate = stock_df['资产周转率'][-2]\n",
    "    asset_turnover_rate_inc = asset_turnover_rate - last_asset_turnover_rate\n",
    "\n",
    "    debt_asset_rate = stock_df['资产负债率'][-1]\n",
    "    last_debt_asset_rate = stock_df['资产负债率'][-2]\n",
    "    debt_asset_rate_inc = debt_asset_rate - last_debt_asset_rate\n",
    "\n",
    "    roe_flag = '增长' if roe_inc > 0 else '下降'\n",
    "    net_rate_flag = '增长' if net_rate_inc > 0 else '下降'\n",
    "    asset_turnover_rate_flag = '增长' if asset_turnover_rate_inc > 0 else '下降'\n",
    "    debt_asset_rate_flag = '增长' if debt_asset_rate_inc > 0 else '下降'\n",
    "\n",
    "    p3_text_1 = f'ROE同期{roe_flag}{\"%.2f\" % (roe_inc*100)}pct至{\"%.2f\" % (roe*100)}%，根据杜邦分析法拆解，'\n",
    "    p3_text_2 = f'净利率同期{net_rate_flag}{\"%.2f\" % (net_rate_inc*100)}pct至{\"%.2f\" % (net_rate*100)}%。'\n",
    "    p3_text_3 = f'资产周转率同期{sale_rate_flag}{\"%.2f\" % (asset_turnover_rate_inc*100)}pct至{\"%.2f\" % (asset_turnover_rate*100)}%，'\n",
    "    p3_text_4 = f'资产负债率同期{debt_asset_rate_flag}{\"%.2f\" % (debt_asset_rate_inc*100)}pct至{\"%.2f\" % (debt_asset_rate*100)}%。'\n",
    "\n",
    "    p3_text = p3_text_1 + p3_text_2 + p3_text_3 + p3_text_4\n",
    "    return p3_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "6755d055-87aa-4ca9-9c3a-311cdfd763f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 这里传参要注意解耦\n",
    "def stock_industry_status(stock_name, stock_id, industry_df, rank_index_list, index_direction_list, rank_threshold):\n",
    "    # 先赋值，再更新\n",
    "    p4_text = ''\n",
    "    # TODO:拉链方法介绍\n",
    "    for index_name, direction in zip(rank_index_list, index_direction_list):\n",
    "        # 注意这里方向是反过来的\n",
    "        tmp_ascending = False if direction else True\n",
    "        tmp_df = industry_df.sort_values(by=index_name, ascending=tmp_ascending)\n",
    "        tmp_rank = tmp_df['股票简称'].tolist().index(stock_name)\n",
    "        if tmp_rank + 1 <= rank_threshold:\n",
    "            tmp_value = tmp_df.loc[stock_id, index_name]\n",
    "            tmp_avg_value = tmp_df[index_name].mean()\n",
    "            direction_flag = '远超过' if direction else '远低于'\n",
    "            p4_text = f'公司的{index_name}表现良好，达到{\"%.2f\" % (tmp_value*100)}%，在行业内排名第{tmp_rank+1}，{direction_flag}行业平均的{\"%.2f\" % (tmp_avg_value*100)}%。'\n",
    "            break\n",
    "    return p4_text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e2146c-9fef-4ca8-babe-36b236ab7186",
   "metadata": {},
   "source": [
    "#### 封装测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "43a68c87-b14f-404b-a355-065ee422249c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设定参数\n",
    "stock_id, report_year = '300750.SZ', '2021'\n",
    "stock_id, report_year = '000820.SZ', '2021'  # 测试行业文字输出\n",
    "stock_id, report_year = '688388.SH', '2020'  # 测试行业文字输出\n",
    "\n",
    "report_date = f'{report_year}-12-31 00:00:00'\n",
    "stock_df = total_df.swaplevel().loc[stock_id].sort_index()\n",
    "stock_df = stock_df.loc[:report_date, :]\n",
    "stock_industry = stock_df['行业名称'][0]\n",
    "stock_data_row = stock_df.shape[0]\n",
    "# 如果只有1行不够！\n",
    "if stock_data_row > 1:\n",
    "    stock_name = stock_df['股票简称'][0]\n",
    "else:  # raise是主动抛出异常\n",
    "    raise Exception('不存在该股票的数据')\n",
    "\n",
    "# 设定重要性指标排序\n",
    "rank_index_list = ['净资产收益率', '利润率', '资产周转率', '资产负债率']\n",
    "# 1表示越大越好， 0表示越小越好\n",
    "index_direction_list = [1, 1, 1, 0]\n",
    "# 设定排名前几输出\n",
    "rank_threshold = 3\n",
    "\n",
    "report_year_df = total_df.loc[report_date]\n",
    "industry_df = report_year_df[report_year_df['行业名称']==stock_industry]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "12549e83-b0d0-466d-b32e-601fbc93ee1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'嘉元科技(688388.SH)全年实现营业总收入12.02亿元，同比下降-16.86%；实现净利润1.86亿元，同比下降-43.45%。'"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_revenue_status(stock_name, stock_id, stock_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "173834ae-c183-4bba-b71b-f903e9a691c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'净利率同期增长4.12pct至19.63%，毛利率同期增长5.79pct至30.03%。费用率方面，销售费用率同期下降-0.07pct至0.28%，管理费用率同期下降-0.83pct至2.18%，财务费用率同期增长2.58pct至1.44%。'"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_efficiency_status(stock_name, stock_id, stock_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "8a6fa60f-e3a5-4a54-a95f-937c8fc052c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ROE同期增长10.44pct至17.65%，根据杜邦分析法拆解，净利率同期增长4.12pct至19.63%。资产周转率同期下降19.33pct至62.30%，资产负债率同期增长29.90pct至40.45%。'"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_finance_status(stock_name, stock_id, stock_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "028aa2b9-a33a-462b-a5e6-73bebba49712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'公司的利润率表现良好，达到19.63%，在行业内排名第3，远超过行业平均的6.89%。'"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传参前少复制了一些代码，导致没有变换行业，所以测试很重要\n",
    "stock_industry_status(stock_name, stock_id, industry_df, rank_index_list, index_direction_list, rank_threshold)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "59ee08f0-b6f7-4d2a-b7d4-79322180c27b",
   "metadata": {},
   "source": [
    "report_year_df.loc[:, ['股票简称', '利润率']].sort_values(by='利润率').dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbfc324f-9279-4dc3-a7bf-7b10bfd83466",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 这里试了几个股票没有出结果，但是测试的时候必须要努力让其有结果，所以多尝试\n",
    "stock_industry_status(stock_name, stock_id, industry_df, rank_index_list, index_direction_list, tmp_rank)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a1139ce-fcf7-488d-b13e-9e8230ddb396",
   "metadata": {},
   "source": [
    "### 文字输出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "da2cac23-e5f4-4000-b6e6-43a4902c6f4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设定参数\n",
    "stock_id, report_year = '300750.SZ', '2021'\n",
    "stock_id, report_year = '000820.SZ', '2021'  # 测试行业文字输出\n",
    "stock_id, report_year = '688388.SH', '2021'  # 测试行业文字输出\n",
    "\n",
    "report_date = f'{report_year}-12-31 00:00:00'\n",
    "stock_df = total_df.swaplevel().loc[stock_id].sort_index()\n",
    "stock_df = stock_df.loc[:report_date, :]\n",
    "stock_industry = stock_df['行业名称'][0]\n",
    "stock_data_row = stock_df.shape[0]\n",
    "# 如果只有1行不够！\n",
    "if stock_data_row > 1:\n",
    "    stock_name = stock_df['股票简称'][0]\n",
    "else:  # raise是主动抛出异常\n",
    "    raise Exception('不存在该股票的数据')\n",
    "\n",
    "# 设定重要性指标排序\n",
    "rank_index_list = ['净资产收益率', '利润率', '资产周转率', '资产负债率']\n",
    "# 1表示越大越好， 0表示越小越好\n",
    "index_direction_list = [1, 1, 1, 0]\n",
    "# 设定排名前几输出\n",
    "rank_threshold = 3\n",
    "\n",
    "report_year_df = total_df.loc[report_date]\n",
    "industry_df = report_year_df[report_year_df['行业名称']==stock_industry]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "b51d73e4-14e2-4d18-9c24-45a8e91b36a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from docx import Document\n",
    "from docx.shared import Inches\n",
    "\n",
    "document = Document()\n",
    "\n",
    "from docx.oxml.ns import qn\n",
    "from docx.shared import Pt \n",
    "# 文字是西文时的字体\n",
    "document.styles['Normal'].font.name = 'Times New Roman'\n",
    "# 文字是中文时的字体\n",
    "document.styles['Normal']._element.rPr.rFonts.set(qn('w:eastAsia'), u'微软雅黑')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "2e67355d-43ed-4ee1-8a44-deee735c079a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 标题部分\n",
    "document.add_heading(f'{stock_name}({stock_id}){report_year}年报分析', 0)\n",
    "# 第一段文字\n",
    "document.add_heading('营收利润情况', level=1)\n",
    "p1_text = stock_revenue_status(stock_name, stock_id, stock_df)\n",
    "document.add_paragraph(p1_text)\n",
    "# 第二段文字\n",
    "document.add_heading('运营效率情况', level=1)\n",
    "p2_text = stock_efficiency_status(stock_name, stock_id, stock_df)\n",
    "document.add_paragraph(p2_text)\n",
    "# 第三段文字\n",
    "document.add_heading('杜邦分析', level=1)\n",
    "p3_text = stock_finance_status(stock_name, stock_id, stock_df)\n",
    "document.add_paragraph(p3_text)\n",
    "# 第四段文字\n",
    "p4_text = stock_industry_status(stock_name, stock_id, industry_df, rank_index_list, index_direction_list, rank_threshold)\n",
    "if p4_text:  # 字符串不为''就是True\n",
    "    document.add_heading('行业情况', level=1)\n",
    "    document.add_paragraph(p4_text)\n",
    "# 保存\n",
    "document.save(f'{stock_name}-{stock_id}-{report_year}年报分析.docx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04e30832-3be0-4da4-bf38-c3c7847b21ef",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 设置字体"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fdff15f-2573-457b-89be-1f55d334bd9c",
   "metadata": {},
   "source": [
    "##### 普通字体"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "6ecccee4-5afa-4dc6-8061-89a60a8ea787",
   "metadata": {},
   "outputs": [],
   "source": [
    "from docx.oxml.ns import qn\n",
    "from docx.shared import Pt \n",
    "# 文字是西文时的字体\n",
    "document.styles['Normal'].font.name = 'Times New Roman'\n",
    "# 文字是中文时的字体\n",
    "document.styles['Normal']._element.rPr.rFonts.set(qn('w:eastAsia'), u'微软雅黑')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53919854-9b29-4483-924a-9a2dbe1348bc",
   "metadata": {},
   "source": [
    "##### 标题问题研究"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b120450c-0589-49b5-861d-04b47b807454",
   "metadata": {},
   "source": [
    "问题：无法设置标题字体"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "167eccf0-7d4a-4ba1-8601-de67b28449ea",
   "metadata": {},
   "source": [
    "参考：https://github.com/python-openxml/python-docx/issues/805"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "f21e7afb-d118-499c-bdd4-bd46e7717068",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<w:style xmlns:w=\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\" xmlns:mc=\"http://schemas.openxmlformats.org/markup-compatibility/2006\" xmlns:r=\"http://schemas.openxmlformats.org/officeDocument/2006/relationships\" xmlns:w14=\"http://schemas.microsoft.com/office/word/2010/wordml\" w:type=\"paragraph\" w:styleId=\"Title\">\\n  <w:name w:val=\"Title\"/>\\n  <w:basedOn w:val=\"Normal\"/>\\n  <w:next w:val=\"Normal\"/>\\n  <w:link w:val=\"TitleChar\"/>\\n  <w:uiPriority w:val=\"10\"/>\\n  <w:qFormat/>\\n  <w:rsid w:val=\"00FC693F\"/>\\n  <w:pPr>\\n    <w:pBdr>\\n      <w:bottom w:val=\"single\" w:sz=\"8\" w:space=\"4\" w:color=\"4F81BD\" w:themeColor=\"accent1\"/>\\n    </w:pBdr>\\n    <w:spacing w:after=\"300\" w:line=\"240\" w:lineRule=\"auto\"/>\\n    <w:contextualSpacing/>\\n  </w:pPr>\\n  <w:rPr>\\n    <w:rFonts w:asciiTheme=\"majorHAnsi\" w:eastAsiaTheme=\"majorEastAsia\" w:hAnsiTheme=\"majorHAnsi\" w:cstheme=\"majorBidi\" w:ascii=\"微软雅黑\" w:hAnsi=\"微软雅黑\"/>\\n    <w:color w:val=\"17365D\" w:themeColor=\"text2\" w:themeShade=\"BF\"/>\\n    <w:spacing w:val'"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ">>> document = Document()\n",
    ">>> heading = document.add_heading(\"Title\", 0)\n",
    ">>> title_style = heading.style\n",
    ">>> title_style.font.name = u\"微软雅黑\"\n",
    ">>> title_style.element.xml[:1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73259cf3-72a6-40dc-9c3e-db0d6971313a",
   "metadata": {},
   "source": [
    "注意：改的是eastAsiaTheme，不要照搬答案，变通点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "fe54a945-eeb1-47d7-8f36-85738c66684c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    ">>> from docx.oxml.ns import qn\n",
    ">>> rFonts = title_style.element.rPr.rFonts\n",
    ">>> rFonts.set(qn(\"w:eastAsiaTheme\"), u\"微软雅黑\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "00839134-95d8-49d4-b21a-7b8805210487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<w:style xmlns:w=\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\" xmlns:mc=\"http://schemas.openxmlformats.org/markup-compatibility/2006\" xmlns:r=\"http://schemas.openxmlformats.org/officeDocument/2006/relationships\" xmlns:w14=\"http://schemas.microsoft.com/office/word/2010/wordml\" w:type=\"paragraph\" w:styleId=\"Title\">\\n  <w:name w:val=\"Title\"/>\\n  <w:basedOn w:val=\"Normal\"/>\\n  <w:next w:val=\"Normal\"/>\\n  <w:link w:val=\"TitleChar\"/>\\n  <w:uiPriority w:val=\"10\"/>\\n  <w:qFormat/>\\n  <w:rsid w:val=\"00FC693F\"/>\\n  <w:pPr>\\n    <w:pBdr>\\n      <w:bottom w:val=\"single\" w:sz=\"8\" w:space=\"4\" w:color=\"4F81BD\" w:themeColor=\"accent1\"/>\\n    </w:pBdr>\\n    <w:spacing w:after=\"300\" w:line=\"240\" w:lineRule=\"auto\"/>\\n    <w:contextualSpacing/>\\n  </w:pPr>\\n  <w:rPr>\\n    <w:rFonts w:asciiTheme=\"majorHAnsi\" w:eastAsiaTheme=\"微软雅黑\" w:hAnsiTheme=\"majorHAnsi\" w:cstheme=\"majorBidi\" w:ascii=\"微软雅黑\" w:hAnsi=\"微软雅黑\"/>\\n    <w:color w:val=\"17365D\" w:themeColor=\"text2\" w:themeShade=\"BF\"/>\\n    <w:spacing w:val=\"5\"/>\\n  '"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ">>> title_style.element.xml[:1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fefcc28b-e95a-44e0-9ba3-2ada82d6a7c2",
   "metadata": {},
   "source": [
    "一行尝试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "a6571b03-a26b-4095-8a86-1a0a28696d6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<w:style xmlns:w=\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\" xmlns:mc=\"http://schemas.openxmlformats.org/markup-compatibility/2006\" xmlns:r=\"http://schemas.openxmlformats.org/officeDocument/2006/relationships\" xmlns:w14=\"http://schemas.microsoft.com/office/word/2010/wordml\" w:type=\"paragraph\" w:styleId=\"Title\">\\n  <w:name w:val=\"Title\"/>\\n  <w:basedOn w:val=\"Normal\"/>\\n  <w:next w:val=\"Normal\"/>\\n  <w:link w:val=\"TitleChar\"/>\\n  <w:uiPriority w:val=\"10\"/>\\n  <w:qFormat/>\\n  <w:rsid w:val=\"00FC693F\"/>\\n  <w:pPr>\\n    <w:pBdr>\\n      <w:bottom w:val=\"single\" w:sz=\"8\" w:space=\"4\" w:color=\"4F81BD\" w:themeColor=\"accent1\"/>\\n    </w:pBdr>\\n    <w:spacing w:after=\"300\" w:line=\"240\" w:lineRule=\"auto\"/>\\n    <w:contextualSpacing/>\\n  </w:pPr>\\n  <w:rPr>\\n    <w:rFonts w:asciiTheme=\"majorHAnsi\" w:eastAsiaTheme=\"宋体\" w:hAnsiTheme=\"majorHAnsi\" w:cstheme=\"majorBidi\" w:ascii=\"宋体\" w:hAnsi=\"宋体\"/>\\n    <w:color w:val=\"17365D\" w:themeColor=\"text2\" w:themeShade=\"BF\"/>\\n    <w:spacing w:val=\"5\"/>\\n    <w:k'"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heading.style.font.name = u\"宋体\"\n",
    "heading.style.element.rPr.rFonts.set(qn(\"w:eastAsiaTheme\"), u\"宋体\")\n",
    "heading.style.element.xml[:1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d736594-a006-430c-bf45-d4712b59873c",
   "metadata": {},
   "source": [
    "##### 测试标题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "69e91e11-b3bc-41bf-bd56-af2c16f1cf81",
   "metadata": {},
   "outputs": [],
   "source": [
    "from docx import Document\n",
    "from docx.shared import Inches\n",
    "\n",
    "document = Document()\n",
    "\n",
    "# 标题部分\n",
    "heading = document.add_heading(f'测试中文字体情况123abc', 0)\n",
    "heading.style.element.rPr.rFonts.set(qn(\"w:eastAsiaTheme\"), u\"宋体\")\n",
    "# 经过多重测试，发现eastAsia也要同时存在！\n",
    "heading.style.element.rPr.rFonts.set(qn(\"w:eastAsia\"), u\"宋体\")\n",
    "\n",
    "document.save(f'中文字体测试.docx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "d7c2561b-a874-4fd3-b6be-482941adbe37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<w:style xmlns:w=\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\" xmlns:mc=\"http://schemas.openxmlformats.org/markup-compatibility/2006\" xmlns:r=\"http://schemas.openxmlformats.org/officeDocument/2006/relationships\" xmlns:w14=\"http://schemas.microsoft.com/office/word/2010/wordml\" w:type=\"paragraph\" w:styleId=\"Title\">\\n  <w:name w:val=\"Title\"/>\\n  <w:basedOn w:val=\"Normal\"/>\\n  <w:next w:val=\"Normal\"/>\\n  <w:link w:val=\"TitleChar\"/>\\n  <w:uiPriority w:val=\"10\"/>\\n  <w:qFormat/>\\n  <w:rsid w:val=\"00FC693F\"/>\\n  <w:pPr>\\n    <w:pBdr>\\n      <w:bottom w:val=\"single\" w:sz=\"8\" w:space=\"4\" w:color=\"4F81BD\" w:themeColor=\"accent1\"/>\\n    </w:pBdr>\\n    <w:spacing w:after=\"300\" w:line=\"240\" w:lineRule=\"auto\"/>\\n    <w:contextualSpacing/>\\n  </w:pPr>\\n  <w:rPr>\\n    <w:rFonts w:asciiTheme=\"majorHAnsi\" w:eastAsiaTheme=\"宋体\" w:hAnsiTheme=\"majorHAnsi\" w:cstheme=\"majorBidi\" w:eastAsia=\"宋体\"/>\\n    <w:color w:val=\"17365D\" w:themeColor=\"text2\" w:themeShade=\"BF\"/>\\n    <w:spacing w:val=\"5\"/>\\n    <w:kern w:val='"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heading.style.element.xml[:1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2110c2a-3f84-4e3e-8dfd-77693e34addb",
   "metadata": {},
   "source": [
    "#### 设置表格"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5636a717-ad6e-4df0-bfec-8304df99a42c",
   "metadata": {},
   "source": [
    "##### 财务摘要"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "91c06c91-6e0b-4419-9b84-77ed533a6c1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "公告日期\n",
       "2017-12-31 00:00:00    5.662286e+08\n",
       "2018-12-31 00:00:00    1.153306e+09\n",
       "2019-12-31 00:00:00    1.446050e+09\n",
       "2020-12-31 00:00:00    1.202179e+09\n",
       "2021-12-31 00:00:00    2.804179e+09\n",
       "Name: 营业收入, dtype: float64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_df['营业收入'][1:]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d822be77-4997-4225-b37e-b26ed2211f21",
   "metadata": {},
   "source": [
    "stock_df['营业收入'] / stock_df['营业收入'].shift(1) - 1 "
   ]
  },
  {
   "cell_type": "raw",
   "id": "34ae8ec1-a379-43a6-a072-c6d1a94e68a7",
   "metadata": {},
   "source": [
    "stock_df['营业收入'].pct_change(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "4d3938bb-7018-40bc-a393-5e991d29effb",
   "metadata": {},
   "outputs": [],
   "source": [
    "revenue_value = stock_df['营业收入'][1:]\n",
    "revenue_rate = stock_df['营业收入'].pct_change(1)[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c35e15e1-9aa3-4cf9-a027-7f51e852952a",
   "metadata": {},
   "source": [
    "EBIT = Earnings Before Interest and Tax  = operation income - I - T  \n",
    "Tax Shield 税盾 = I x tax rate  \n",
    "所得税和税金的区别：https://lvlin.baidu.com/question/401033506612291445.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "d31f747b-c109-48b8-ac42-8657c09990b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "EBIT_value = stock_df['息税前利润'][1:]\n",
    "EBIT_rate = stock_df['息税前利润'].pct_change(1)[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "a2621021-66a7-4210-8589-3ec7bbf5055b",
   "metadata": {},
   "outputs": [],
   "source": [
    "net_income_value = stock_df['净利润'][1:]\n",
    "net_income_rate = stock_df['净利润'].pct_change(1)[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "3d644d6e-559e-4838-944a-bf1ec49e72ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "finance_table = pd.concat([revenue_value, revenue_rate, EBIT_value, EBIT_rate, net_income_value, net_income_value], axis=1).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "d9c96c77-3dc4-4e4b-b34b-80089245ac9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>公告日期</th>\n",
       "      <th>2017-12-31 00:00:00</th>\n",
       "      <th>2018-12-31 00:00:00</th>\n",
       "      <th>2019-12-31 00:00:00</th>\n",
       "      <th>2020-12-31 00:00:00</th>\n",
       "      <th>2021-12-31 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>营业收入</th>\n",
       "      <td>5.662286e+08</td>\n",
       "      <td>1.153306e+09</td>\n",
       "      <td>1.446050e+09</td>\n",
       "      <td>1.202179e+09</td>\n",
       "      <td>2.804179e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>营业收入</th>\n",
       "      <td>3.521099e-01</td>\n",
       "      <td>1.036820e+00</td>\n",
       "      <td>2.538305e-01</td>\n",
       "      <td>-1.686462e-01</td>\n",
       "      <td>1.332581e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>息税前利润</th>\n",
       "      <td>1.070986e+08</td>\n",
       "      <td>2.288438e+08</td>\n",
       "      <td>4.004262e+08</td>\n",
       "      <td>1.976127e+08</td>\n",
       "      <td>6.718586e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>息税前利润</th>\n",
       "      <td>3.833239e-01</td>\n",
       "      <td>1.136758e+00</td>\n",
       "      <td>7.497796e-01</td>\n",
       "      <td>-5.064941e-01</td>\n",
       "      <td>2.399876e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>净利润</th>\n",
       "      <td>8.519251e+07</td>\n",
       "      <td>1.764311e+08</td>\n",
       "      <td>3.297248e+08</td>\n",
       "      <td>1.864722e+08</td>\n",
       "      <td>5.505694e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>净利润</th>\n",
       "      <td>8.519251e+07</td>\n",
       "      <td>1.764311e+08</td>\n",
       "      <td>3.297248e+08</td>\n",
       "      <td>1.864722e+08</td>\n",
       "      <td>5.505694e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "公告日期   2017-12-31 00:00:00  2018-12-31 00:00:00  2019-12-31 00:00:00  \\\n",
       "营业收入          5.662286e+08         1.153306e+09         1.446050e+09   \n",
       "营业收入          3.521099e-01         1.036820e+00         2.538305e-01   \n",
       "息税前利润         1.070986e+08         2.288438e+08         4.004262e+08   \n",
       "息税前利润         3.833239e-01         1.136758e+00         7.497796e-01   \n",
       "净利润           8.519251e+07         1.764311e+08         3.297248e+08   \n",
       "净利润           8.519251e+07         1.764311e+08         3.297248e+08   \n",
       "\n",
       "公告日期   2020-12-31 00:00:00  2021-12-31 00:00:00  \n",
       "营业收入          1.202179e+09         2.804179e+09  \n",
       "营业收入         -1.686462e-01         1.332581e+00  \n",
       "息税前利润         1.976127e+08         6.718586e+08  \n",
       "息税前利润        -5.064941e-01         2.399876e+00  \n",
       "净利润           1.864722e+08         5.505694e+08  \n",
       "净利润           1.864722e+08         5.505694e+08  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finance_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "4b36b4ed-ce2b-4014-9964-c00a418b5270",
   "metadata": {},
   "outputs": [],
   "source": [
    "finance_table.columns = [i[:4]+'A' for i in finance_table.columns]\n",
    "finance_table.index = ['营业收入', '(+/-)%', '经营利润(EBIT)', '(+/-)%', '净利润', '(+/-)%']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "8a578f6f-9c1c-4dd6-ae20-0f19db123eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2017A</th>\n",
       "      <th>2018A</th>\n",
       "      <th>2019A</th>\n",
       "      <th>2020A</th>\n",
       "      <th>2021A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>营业收入</th>\n",
       "      <td>5.662286e+08</td>\n",
       "      <td>1.153306e+09</td>\n",
       "      <td>1.446050e+09</td>\n",
       "      <td>1.202179e+09</td>\n",
       "      <td>2.804179e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(+/-)%</th>\n",
       "      <td>3.521099e-01</td>\n",
       "      <td>1.036820e+00</td>\n",
       "      <td>2.538305e-01</td>\n",
       "      <td>-1.686462e-01</td>\n",
       "      <td>1.332581e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>经营利润(EBIT)</th>\n",
       "      <td>1.070986e+08</td>\n",
       "      <td>2.288438e+08</td>\n",
       "      <td>4.004262e+08</td>\n",
       "      <td>1.976127e+08</td>\n",
       "      <td>6.718586e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(+/-)%</th>\n",
       "      <td>3.833239e-01</td>\n",
       "      <td>1.136758e+00</td>\n",
       "      <td>7.497796e-01</td>\n",
       "      <td>-5.064941e-01</td>\n",
       "      <td>2.399876e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>净利润</th>\n",
       "      <td>8.519251e+07</td>\n",
       "      <td>1.764311e+08</td>\n",
       "      <td>3.297248e+08</td>\n",
       "      <td>1.864722e+08</td>\n",
       "      <td>5.505694e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(+/-)%</th>\n",
       "      <td>8.519251e+07</td>\n",
       "      <td>1.764311e+08</td>\n",
       "      <td>3.297248e+08</td>\n",
       "      <td>1.864722e+08</td>\n",
       "      <td>5.505694e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   2017A         2018A         2019A         2020A  \\\n",
       "营业收入        5.662286e+08  1.153306e+09  1.446050e+09  1.202179e+09   \n",
       "(+/-)%      3.521099e-01  1.036820e+00  2.538305e-01 -1.686462e-01   \n",
       "经营利润(EBIT)  1.070986e+08  2.288438e+08  4.004262e+08  1.976127e+08   \n",
       "(+/-)%      3.833239e-01  1.136758e+00  7.497796e-01 -5.064941e-01   \n",
       "净利润         8.519251e+07  1.764311e+08  3.297248e+08  1.864722e+08   \n",
       "(+/-)%      8.519251e+07  1.764311e+08  3.297248e+08  1.864722e+08   \n",
       "\n",
       "                   2021A  \n",
       "营业收入        2.804179e+09  \n",
       "(+/-)%      1.332581e+00  \n",
       "经营利润(EBIT)  6.718586e+08  \n",
       "(+/-)%      2.399876e+00  \n",
       "净利润         5.505694e+08  \n",
       "(+/-)%      5.505694e+08  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finance_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "7a5d8949-ec5a-4d68-831b-8f0305535679",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_finance_table(stock_df):\n",
    "    revenue_value = stock_df['营业收入'][1:]\n",
    "    revenue_rate = stock_df['营业收入'].pct_change(1)[1:]\n",
    "    EBIT_value = stock_df['息税前利润'][1:]\n",
    "    EBIT_rate = stock_df['息税前利润'].pct_change(1)[1:]\n",
    "    net_income_value = stock_df['净利润'][1:]\n",
    "    net_income_rate = stock_df['净利润'].pct_change(1)[1:]\n",
    "    finance_table = pd.concat([revenue_value, revenue_rate, EBIT_value, EBIT_rate, net_income_value, net_income_rate], axis=1).T\n",
    "    finance_table.columns = finance_table.columns.str.slice(0, 4) + 'A'\n",
    "    finance_table.index = ['营业收入', '(+/-)%', '经营利润(EBIT)', '(+/-)%', '净利润', '(+/-)%']\n",
    "    return finance_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b49742-569e-4a4a-83f0-19a4f4f4921f",
   "metadata": {},
   "source": [
    "##### 估值指标"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "080471a7-00c4-43cb-8f04-6423bdabd853",
   "metadata": {},
   "source": [
    "业绩快报包含每股盈利：https://data.eastmoney.com/bbsj/201806/yjkb.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7988a12-9aeb-4c31-93ef-778da04cd333",
   "metadata": {},
   "source": [
    "这里没有获取股票当前价格、股票数量，所以就先用杜邦分析的结果简单替代，也不显示增减了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "6239cc06-44f2-49d3-8b7f-2afcb341e5a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 尽管不用算增速可以多1条数据，但是也要和其他数据对齐\n",
    "roe = stock_df['净资产收益率'][1:]\n",
    "net_rate = stock_df['利润率'][1:]\n",
    "asset_turnover_rate = stock_df['资产周转率'][1:]\n",
    "debt_asset_rate = stock_df['资产负债率'][1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "61882d16-27a4-43e8-badf-d13ea13ca8a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dupont_table = pd.concat([roe, net_rate ,asset_turnover_rate, debt_asset_rate], axis=1).T\n",
    "dupont_table.columns = dupont_table.columns.str.slice(0, 4) + 'A'\n",
    "dupont_table.index = ['净资产收益率(ROE)', '利润率', '资产周转率', '资产负债率']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "af82bfc0-3e34-4cb4-918a-c11c9c72fe6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>公告日期</th>\n",
       "      <th>2017A</th>\n",
       "      <th>2018A</th>\n",
       "      <th>2019A</th>\n",
       "      <th>2020A</th>\n",
       "      <th>2021A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>净资产收益率(ROE)</th>\n",
       "      <td>0.175465</td>\n",
       "      <td>0.286824</td>\n",
       "      <td>0.203100</td>\n",
       "      <td>0.072075</td>\n",
       "      <td>0.176477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>利润率</th>\n",
       "      <td>0.150456</td>\n",
       "      <td>0.152979</td>\n",
       "      <td>0.228018</td>\n",
       "      <td>0.155112</td>\n",
       "      <td>0.196339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>资产周转率</th>\n",
       "      <td>0.828743</td>\n",
       "      <td>1.272895</td>\n",
       "      <td>0.788657</td>\n",
       "      <td>0.429725</td>\n",
       "      <td>0.623027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>资产负债率</th>\n",
       "      <td>0.340333</td>\n",
       "      <td>0.305935</td>\n",
       "      <td>0.041515</td>\n",
       "      <td>0.105585</td>\n",
       "      <td>0.404538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "公告日期            2017A     2018A     2019A     2020A     2021A\n",
       "净资产收益率(ROE)  0.175465  0.286824  0.203100  0.072075  0.176477\n",
       "利润率          0.150456  0.152979  0.228018  0.155112  0.196339\n",
       "资产周转率        0.828743  1.272895  0.788657  0.429725  0.623027\n",
       "资产负债率        0.340333  0.305935  0.041515  0.105585  0.404538"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dupont_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "b072c6ef-98b4-45e8-b461-a37b71a0e196",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dupont_table(stock_df):\n",
    "    # 尽管不用算增速可以多1条数据，但是也要和其他数据对齐\n",
    "    roe = stock_df['净资产收益率'][1:]\n",
    "    net_rate = stock_df['利润率'][1:]\n",
    "    asset_turnover_rate = stock_df['资产周转率'][1:]\n",
    "    debt_asset_rate = stock_df['资产负债率'][1:]\n",
    "    dupont_table = pd.concat([roe, net_rate ,asset_turnover_rate, debt_asset_rate], axis=1).T\n",
    "    dupont_table.columns = dupont_table.columns.str.slice(0, 4) + 'A'\n",
    "    dupont_table.index = ['净资产收益率(ROE)', '利润率', '资产周转率', '资产负债率']\n",
    "    return dupont_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f503daf-846a-4ba9-bd48-21471e7a4b86",
   "metadata": {},
   "source": [
    "#### 文字部分合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "97cf8e6c-36ac-4d0f-a91f-1ee2910a1ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将改变字体的函数进行封装\n",
    "def change_font(h, font_name=u'微软雅黑'):\n",
    "    h.style.element.rPr.rFonts.set(qn(\"w:eastAsia\"), font_name)\n",
    "    h.style.element.rPr.rFonts.set(qn(\"w:eastAsiaTheme\"), font_name)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "637d2741-e6e4-4e9c-9432-288706d76ff1",
   "metadata": {},
   "source": [
    "table = document.add_table(rows=1, cols=3)\n",
    "hdr_cells = table.rows[0].cells\n",
    "hdr_cells[0].text = 'Qty'\n",
    "hdr_cells[1].text = 'Id'\n",
    "hdr_cells[2].text = 'Desc'\n",
    "for qty, id, desc in records:\n",
    "    row_cells = table.add_row().cells\n",
    "    row_cells[0].text = str(qty)\n",
    "    row_cells[1].text = id\n",
    "    row_cells[2].text = desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "b19cb91e-cb93-4d1e-a40b-8097a7baf5e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将表格保存的函数进行封装\n",
    "def add_df_document(df, doc, table_name):\n",
    "    table = doc.add_table(rows=df.shape[0]+1, cols=df.shape[1]+1, style='Light Shading Accent 1')\n",
    "    # 增加表头\n",
    "    hdr_cells = table.rows[0].cells\n",
    "    # 第一行的第一个格子填充表名\n",
    "    hdr_cells[0].text = table_name\n",
    "    for col_idx, col_name in enumerate(df.columns):\n",
    "        hdr_cells[col_idx+1].text = col_name\n",
    "    # 增加内容，先按行循环\n",
    "    for idx_idx, idx_name in enumerate(df.index):\n",
    "        # 增加一行\n",
    "        row_cells = table.rows[idx_idx+1].cells\n",
    "        # 给这行填充列的值，先填充这行的指标名称\n",
    "        row_cells[0].text = idx_name\n",
    "        for col_idx, col_name in enumerate(df.columns):\n",
    "            tmp_value = df.iloc[idx_idx, col_idx]\n",
    "            # 如果指标名称有比率的情况\n",
    "            if ('%' in idx_name) or ('率' in idx_name):\n",
    "                row_cells[col_idx+1].text = \"%.2f\" % (tmp_value*100)\n",
    "            else:  # 否则就简单按百万计算\n",
    "                row_cells[col_idx+1].text = str(int(tmp_value / 1e6))\n",
    "    return doc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "0f51ec8f-f077-47fc-9d87-86fbe2a92d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from docx import Document\n",
    "from docx.shared import Inches\n",
    "from docx.oxml.ns import qn\n",
    "from docx.shared import Pt \n",
    "\n",
    "document = Document()\n",
    "\n",
    "# 修改字体\n",
    "document.styles['Normal'].font.name = 'Times New Roman'\n",
    "document.styles['Normal']._element.rPr.rFonts.set(qn('w:eastAsia'), u'微软雅黑')\n",
    "\n",
    "# 标题部分\n",
    "heading = document.add_heading(f'{stock_name}({stock_id}){report_year}年报分析', 0)\n",
    "change_font(heading)\n",
    "\n",
    "# 第一段文字\n",
    "heading1 = document.add_heading('营收利润情况', level=1)\n",
    "change_font(heading1)\n",
    "p1_text = stock_revenue_status(stock_name, stock_id, stock_df)\n",
    "document.add_paragraph(p1_text)\n",
    "# 第二段文字\n",
    "heading2 = document.add_heading('运营效率情况', level=1)\n",
    "change_font(heading2)\n",
    "p2_text = stock_efficiency_status(stock_name, stock_id, stock_df)\n",
    "document.add_paragraph(p2_text)\n",
    "# 第三段文字\n",
    "heading3 = document.add_heading('杜邦分析', level=1)\n",
    "change_font(heading3)\n",
    "p3_text = stock_finance_status(stock_name, stock_id, stock_df)\n",
    "document.add_paragraph(p3_text)\n",
    "# 第四段文字\n",
    "p4_text = stock_industry_status(stock_name, stock_id, industry_df, rank_index_list, index_direction_list, rank_threshold)\n",
    "if p4_text:  # 字符串不为''就是True\n",
    "    heading4 = document.add_heading('行业情况', level=1)\n",
    "    change_font(heading4)\n",
    "    document.add_paragraph(p4_text)\n",
    "\n",
    "# 翻页获取表格\n",
    "document.add_page_break()\n",
    "finance_table = get_finance_table(stock_df)\n",
    "dupont_table = get_dupont_table(stock_df)\n",
    "document = add_df_document(finance_table, document, '财务摘要（百万元）')\n",
    "document.add_paragraph('\\n')\n",
    "document = add_df_document(dupont_table, document, '杜邦分析指标')\n",
    "\n",
    "# 保存\n",
    "document.save(f'{stock_name}-{stock_id}-{report_year}年报分析.docx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08470bdc-5836-4389-b681-5fd40317de3b",
   "metadata": {},
   "source": [
    "## 图表准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "bcac14f5-8b38-40fa-94d3-67cd6cd56854",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b60c027-64d6-4080-947b-c6604b6bff25",
   "metadata": {},
   "source": [
    "### 营收净利双轴"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0bf51ea-eac9-477a-a188-d4be43aa763a",
   "metadata": {},
   "source": [
    "#### 参考代码"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9ad7a86-238e-4ceb-8b9d-d86f8f3a958e",
   "metadata": {},
   "source": [
    "https://blog.csdn.net/lanhezhong/article/details/108061483"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "748a55d3-7601-49bc-b9c8-663ea02fa797",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    " \n",
    "plt.rcParams['font.sans-serif']=['SimHei']#用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False#用来正常显示负号\n",
    " \n",
    "if __name__ == '__main__':\n",
    "    X=[\"2019-01\", \"2019-02\", \"2019-03\", \"2019-04\", \"2019-05\", \"2019-06\", \"2019-07\", \"2019-08\", \"2019-09\", \"2019-10\", \"2019-11\", \"2019-12\"]\n",
    "    Y1=np.random.randint(500,1000,12)\n",
    "    Y2=[None]\n",
    "    for i in range(1,len(Y1)):\n",
    "        Y2.append((Y1[i]-Y1[i-1])/Y1[i-1])\n",
    " \n",
    "    fig, ax1 = plt.subplots()\n",
    "    plt.xticks(rotation=45)\n",
    " \n",
    "    ax1.bar(X, Y1, color=\"blue\", alpha=0.5, label=\"出货量\")\n",
    "    ax1.set_xlabel(\"月份\")\n",
    "    ax1.set_ylabel(\"出货量(吨)\")\n",
    " \n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.plot(X, Y2, color=\"red\", label=\"环比增长\")\n",
    "    ax2.set_ylabel(\"环比增长\")\n",
    " \n",
    "    fig.legend(loc=\"upper right\", bbox_to_anchor=(0.5, 1), bbox_transform=ax1.transAxes)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7afe1cbe-b16b-49c2-ae65-1b6a40c6b09e",
   "metadata": {},
   "source": [
    "官方案例：https://matplotlib.org/stable/gallery/axisartist/demo_parasite_axes2.html#sphx-glr-gallery-axisartist-demo-parasite-axes2-py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "b572c726-d25d-4b96-82d6-a421c6960b90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1 import host_subplot\n",
    "from mpl_toolkits import axisartist\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "host = host_subplot(111)\n",
    "\n",
    "par1 = host.twinx()\n",
    "\n",
    "p1, = host.plot([0, 1, 2], [0, 1, 2], label=\"Density\")\n",
    "p2, = par1.plot([0, 1, 2], [0, 3, 2], label=\"Temperature\")\n",
    "\n",
    "host.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e88c5e78-a277-468c-b4f3-53e4b4e0b85d",
   "metadata": {},
   "source": [
    "Matplotlib 的默认颜色 以及 使用调色盘调整颜色"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a0a0b1a-0db1-4e49-aee4-d9d19f5ff9f5",
   "metadata": {},
   "source": [
    "https://www.cnblogs.com/Gelthin2017/p/14177100.html  \n",
    "https://matplotlib.org/3.1.1/users/dflt_style_changes.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e75cec-9d24-4ba8-a143-711f0c172104",
   "metadata": {},
   "source": [
    "#### 实现代码"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b23d79-fc65-4be4-a7e1-fd95cc4d5d39",
   "metadata": {},
   "source": [
    "图像的内容尽量清晰简洁、不要有歧义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "398a96dc-5d35-4d4c-9bed-ddaca6758cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "revenue_value = stock_df['营业收入'][1:]\n",
    "revenue_rate = stock_df['营业收入'].pct_change(1)[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "034def63-acfe-4176-bb0c-33879a05d712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21466886eb0>"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import host_subplot\n",
    "\n",
    "x = revenue_value.index.str.slice(0, 4)\n",
    "y1 = revenue_value.values / 1e8\n",
    "y2 = revenue_rate.values * 100\n",
    "\n",
    "host = host_subplot(111)\n",
    "par1 = host.twinx()\n",
    "host.bar(x,y1,color='C0', label='营业收入(亿元)')\n",
    "par1.plot(x,y2, color='C1', label='营业收入yoy（%）', linewidth=3)\n",
    "host.legend(loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "96a017ed-3269-4435-8fee-c36e3e4689bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "net_income_value = stock_df['净利润'][1:]\n",
    "net_income_rate = stock_df['净利润'].pct_change(1)[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "8a67fb2d-435a-44db-8aab-642983c92fed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21466a57d00>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import host_subplot\n",
    "\n",
    "x = net_income_value.index.str.slice(0, 4)\n",
    "y1 = net_income_value.values / 1e8\n",
    "y2 = net_income_rate.values * 100\n",
    "\n",
    "host = host_subplot(111)\n",
    "par1 = host.twinx()\n",
    "host.bar(x,y1,color='C0', label='净利润(亿元)')\n",
    "par1.plot(x,y2, color='C1', label='净利润yoy（%）', linewidth=3)\n",
    "host.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4cf39b2-1598-4d14-b2cd-95524584e26f",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 降本提效"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "2261e820-f53e-4ca5-acf2-748ae481dd44",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 尽管不用算增速可以多1条数据，但是也要和其他数据对齐\n",
    "net_rate = stock_df['利润率'][1:]\n",
    "gross_rate = stock_df['毛利率'][1:]\n",
    "sale_rate = stock_df['销售费用率'][1:]\n",
    "admin_rate = stock_df['管理费用率'][1:]\n",
    "finance_rate = stock_df['财务费用率'][1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "02113e84-1315-4544-95b1-920d175c7a7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21466cb7a60>"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = revenue_value.index.str.slice(0, 4)\n",
    "y0 = net_rate.values * 100\n",
    "y1 = gross_rate.values * 100\n",
    "fig = plt.figure()\n",
    "plt.plot(x, y0, label='净利率（%）')\n",
    "plt.plot(x, y1, label='毛利率（%）')\n",
    "plt.legend(loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "f32673f4-eff5-441a-8031-c1f5265a003e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21466d7d1f0>"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = sale_rate.index.str.slice(0, 4)\n",
    "y2 = sale_rate.values * 100\n",
    "y3 = admin_rate.values * 100\n",
    "y4 = finance_rate.values * 100\n",
    "fig = plt.figure()\n",
    "plt.plot(x, y2, label='销售费用率（%）')\n",
    "plt.plot(x, y3, label='管理费用率（%）')\n",
    "plt.plot(x, y4, label='财务费用率（%）')\n",
    "plt.legend(loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05a3cdf1-52b2-4a2d-9137-e82ec90df32b",
   "metadata": {},
   "source": [
    "### 杜邦分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fbd56f3-d4d9-47fa-891e-eacdd6e3da76",
   "metadata": {},
   "source": [
    "方向不同的不要放在一个图，比如上面的利润一起费用一起，这里是ROE和周转率一起，然后负债率单独"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "9b9fbcca-03ea-4ce7-a817-3d00142dc07e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 尽管不用算增速可以多1条数据，但是也要和其他数据对齐\n",
    "roe = stock_df['净资产收益率'][1:]\n",
    "asset_turnover_rate = stock_df['资产周转率'][1:]\n",
    "net_rate = stock_df['利润率'][1:]\n",
    "debt_asset_rate = stock_df['资产负债率'][1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "a450d162-add9-4753-b65f-45c2d8e4a016",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21469e57640>"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = revenue_value.index.str.slice(0, 4)\n",
    "y0 = roe.values * 100\n",
    "y1 = asset_turnover_rate.values * 100\n",
    "y2 = net_rate.values * 100\n",
    "fig = plt.figure()\n",
    "plt.plot(x, y0, label='ROE（%）')\n",
    "plt.plot(x, y1, label='资产周转率（%）')\n",
    "plt.plot(x, y2, label='净利率（%）')\n",
    "plt.legend(loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "9863a0b1-219e-4e91-b556-568cf8019fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21469eccd30>"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = revenue_value.index.str.slice(0, 4)\n",
    "y2 = debt_asset_rate.values * 100\n",
    "fig = plt.figure()\n",
    "plt.plot(x, y2, label='资产负债率（%）')\n",
    "plt.legend(loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b5d367d-e128-4166-9af5-0f98ac83fc4f",
   "metadata": {},
   "source": [
    "### 图表保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "f6aae88f-6cb3-4b25-b3ff-35d463b9cc6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = revenue_value.index.str.slice(0, 4)\n",
    "y2 = debt_asset_rate.values * 100\n",
    "fig = plt.figure()\n",
    "plt.plot(x, y2, label='资产负债率（%）')\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.savefig('测试图片保存.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72dd5ebc-e3bd-45ec-9cae-3f99f6224ea7",
   "metadata": {},
   "source": [
    "### 函数封装"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "39edfcac-6cf8-4353-8544-6ba53ecb59d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_revenue_pic(stock_df, pic_name):\n",
    "    # 导包\n",
    "    import matplotlib.pyplot as plt\n",
    "    from mpl_toolkits.axes_grid1 import host_subplot\n",
    "    # 计算\n",
    "    revenue_value = stock_df['营业收入'][1:]\n",
    "    revenue_rate = stock_df['营业收入'].pct_change(1)[1:]\n",
    "    x = revenue_value.index.str.slice(0, 4)\n",
    "    y1 = revenue_value.values / 1e8\n",
    "    y2 = revenue_rate.values * 100\n",
    "    # 画图\n",
    "    host = host_subplot(111)\n",
    "    par1 = host.twinx()\n",
    "    host.bar(x,y1,color='C0', label='营业收入(亿元)')\n",
    "    par1.plot(x,y2, color='C1', label='营业收入yoy（%）', linewidth=3)\n",
    "    host.legend(loc=\"upper left\")\n",
    "    plt.savefig(pic_name)\n",
    "    # 清空画布\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "cf353497-215e-4ae9-8466-2d22acce8d1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_net_income_pic(stock_df, pic_name):\n",
    "    # 导包\n",
    "    import matplotlib.pyplot as plt\n",
    "    from mpl_toolkits.axes_grid1 import host_subplot\n",
    "    # 计算\n",
    "    net_income_value = stock_df['净利润'][1:]\n",
    "    net_income_rate = stock_df['净利润'].pct_change(1)[1:]\n",
    "    x = net_income_value.index.str.slice(0, 4)\n",
    "    y1 = net_income_value.values / 1e8\n",
    "    y2 = net_income_rate.values * 100\n",
    "    # 画图\n",
    "    host = host_subplot(111)\n",
    "    par1 = host.twinx()\n",
    "    host.bar(x,y1,color='C0', label='净利润(亿元)')\n",
    "    par1.plot(x,y2, color='C1', label='净利润yoy（%）', linewidth=3)\n",
    "    host.legend(loc=\"upper left\")\n",
    "    plt.savefig(pic_name)\n",
    "    # 清空画布\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "5ae10dcb-0d8f-4f7d-a7ca-2a3c1580ab09",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_profit_rate_pic(stock_df, pic_name):\n",
    "    # 导包\n",
    "    import matplotlib.pyplot as plt\n",
    "    # 计算\n",
    "    net_rate = stock_df['利润率'][1:]\n",
    "    gross_rate = stock_df['毛利率'][1:]\n",
    "    # 画图\n",
    "    x = net_rate.index.str.slice(0, 4)\n",
    "    y0 = net_rate.values * 100\n",
    "    y1 = gross_rate.values * 100\n",
    "    fig = plt.figure()\n",
    "    plt.plot(x, y0, label='利润率（%）')\n",
    "    plt.plot(x, y1, label='毛利率（%）')\n",
    "    plt.legend(loc=\"upper left\")\n",
    "    plt.savefig(pic_name)\n",
    "    # 清空画布\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "bb872ea7-f1ce-4476-9141-909da2842529",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_cost_rate_pic(stock_df, pic_name):\n",
    "    # 导包\n",
    "    import matplotlib.pyplot as plt\n",
    "    # 计算\n",
    "    sale_rate = stock_df['销售费用率'][1:]\n",
    "    admin_rate = stock_df['管理费用率'][1:]\n",
    "    finance_rate = stock_df['财务费用率'][1:]\n",
    "    # 画图\n",
    "    x = sale_rate.index.str.slice(0, 4)\n",
    "    y2 = sale_rate.values * 100\n",
    "    y3 = admin_rate.values * 100\n",
    "    y4 = finance_rate.values * 100\n",
    "    fig = plt.figure()\n",
    "    plt.plot(x, y2, label='销售费用率（%）')\n",
    "    plt.plot(x, y3, label='管理费用率（%）')\n",
    "    plt.plot(x, y4, label='财务费用率（%）')\n",
    "    plt.legend(loc=\"upper left\")\n",
    "    plt.savefig(pic_name)\n",
    "    # 清空画布\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "822a77f0-cd6e-4762-877a-4a84491ae16b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_dupont1_pic(stock_df, pic_name):\n",
    "    # 导包\n",
    "    import matplotlib.pyplot as plt\n",
    "    # 计算\n",
    "    roe = stock_df['净资产收益率'][1:]\n",
    "    asset_turnover_rate = stock_df['资产周转率'][1:]\n",
    "    net_rate = stock_df['利润率'][1:]\n",
    "    debt_asset_rate = stock_df['资产负债率'][1:]\n",
    "    # 画图\n",
    "    x = roe.index.str.slice(0, 4)\n",
    "    y0 = roe.values * 100\n",
    "    y1 = asset_turnover_rate.values * 100\n",
    "    y2 = net_rate.values * 100\n",
    "    fig = plt.figure()\n",
    "    plt.plot(x, y0, label='ROE（%）')\n",
    "    plt.plot(x, y1, label='资产周转率（%）')\n",
    "    plt.plot(x, y2, label='利润率（%）')\n",
    "    plt.legend(loc=\"upper left\")\n",
    "    plt.savefig(pic_name)\n",
    "    # 清空画布\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "c86ced77-9d48-4aeb-ba05-7f8a8bdd935b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_dupont2_pic(stock_df, pic_name):\n",
    "    # 导包\n",
    "    import matplotlib.pyplot as plt\n",
    "    # 计算\n",
    "    roe = stock_df['净资产收益率'][1:]\n",
    "    asset_turnover_rate = stock_df['资产周转率'][1:]\n",
    "    net_rate = stock_df['利润率'][1:]\n",
    "    debt_asset_rate = stock_df['资产负债率'][1:]\n",
    "    # 画图\n",
    "    x = roe.index.str.slice(0, 4)\n",
    "    y2 = debt_asset_rate.values * 100\n",
    "    fig = plt.figure()\n",
    "    plt.plot(x, y2, label='资产负债率（%）')\n",
    "    plt.legend(loc=\"upper left\")\n",
    "    plt.savefig(pic_name)\n",
    "    # 清空画布\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "dac23227-4d11-4bfc-9195-1e60ab806663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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B3I3Ut1EqAzhzkXUU0FlElgO+QBmgBjDQMTumnYhUFpHBibYbBEwENgArjTFLXC0uKCiIM2fOZM0AyiKMMZw5c4agoCC7S3HeuQMw7kmIPg9dZkGxmjYX5IR8JaDlN3A0ApZ+anc1Kpvw6BWdbty4wZEjR4iO1uZN7hQUFESxYsXw9/e3u5TUnd1vzXOPuQRdfoV7HrC7Itf8+gqs/8mqvczDdlejvEBKp2g8+k4Yf39/SpfW85XK4ew++PEpuHEFus6Gu6vZXZHrHv8CDq2y7nJ94S8ILmh3RcqLaasClTWc2WudlrlxFbr+ljXDHSAg2GplcPUMzH5FV4FSbqUBrzzf6T3WBdW4GOg2x2oSl5XdXc1qSrZzHqwdY3c1yotpwCvPdmqXI9xvQNc5ULiK3RVljLovQrmmsHCgNX1SKTfQgFee69ROK9xNPHSbC4Ur211RxhGxFggJyutYBeqa3RUpL6QBrzxT1A4r3EWscL+rkt0VZbxcIfDMCDi13TqSVyqDacArz3NymyPcfa1wD6lgd0XuU+4RqP8qRITB9t/srkZ5GQ145VlObLHaD/j6W+FeqLzdFblfkw/g7gdg9qtw4ajd1SgvogGvPMeJzdZNTH5BjnAvZ3dFmcMvANqOhdjrMKMPxMfZXZHyEhrwyjMc32iFu39OaypkwbJ2V5S5CpaFJ76Cg3/Cn0PtrkZ5CQ14Zb9j62F8SwjIZYV7gTJ2V2SPB56H+9rC0s/g8Bq7q1FeQANe2evoOpjwNATmsU7LZOdWuiLQYijkLQrTe0L0hdS3USoFGvDKPkciYUIrCMoH3edC/pJ2V2S/oLzQZqx1sXXOv7SVgUoXDXhlj8NrYWIryJnfOnLPV8LuijxH8VrQeABsmQ4bfrG7GpWFacCrzHd4DUx8BnIWhG7zIF/SSxpmaw+9CaUawLx+Vi8epdJAA15lrkOrrHDPdRd0n2edb1Z38vGF1qOsKZTTe0BsjN0VqSzIqYAXkQIi0lRECrm7IOXFDv4NE1tD7rut0zJ57rG7Is+W5x54+jtrCunvg+yuRmVBzqzJmh+YA9QGlopIiIiEichKEXk/lW2dGqeygQN/wk9trSP2bnMgz912V5Q1VHoSQnvCyv/CHpdXvVTZnDNH8FWBN40xQ4CFQBPA1xhTDygjIkneSy4irZ0Zp7KB/Svg52etc+1d50DuInZXlLU0HwIh98LMF+FylN3VqIwUFwuLP7QmHbhBqgFvjPnDGLNKRBpiHcU3B6Y4vrwIeCiZTRs5OU55s33LHOFe0hHuhe2uKOvxz2G1Moi5CLNehPh4uytSGeHSCevu7b/+47a/zpw9By9AO+AcYICEjkhngeR+YoNTGycifUQkQkQiTp065UrdKivYuxR+aWfdmdptjtUeV6VN4crQbLAVBKt/sLsalV77l8OIBnB8A7QebU2LdQOnAt5YXgY2AfWBHI4v5UrhNS6nNs4YM8oYE2qMCQ0J0R9+r7LndwhvDwXLWWuoBuv1+XSr1QsqPmn9SX98o93VqLSIj4flX1t3b+fIB72XQtXn3PZ2zlxkfVdEujie5gM+55/TLdWAA8lsGunkOOVtdi+B8A5Wq9+uv0FwQbsr8g4i0HK49ctyWg+IuWx3RcoVV89CeDv43ydQpbUV7m5eyMaZI/hRQGcRWQ74ArMcz4cCzwFzRaSyiAxOtN0d4zKsauW5di2CSR0gpCJ0mQ05C9hdkXcJLmjNjz+zFxa8a3c1yllHI2Hkw9Y1qSf/DW3GQGAut7+tMxdZzxljmhpjGhpjXjLGXMC6gLoKaGyMuWCM2WaMeT/RdhcTj8v48pVH2bkAJneEuypDVw13tyndEBq8Cet/gi0z7K5GpcQYWDMawppbz3sssE61iWTK2/ulZSNjzDn+mSGT7nHKC+yYC1O6QpH7ofNM6/yicp9GA2DfH/DbG1C0pjZq80Qxl+G316yeQuWbW+vvZvJBj7YqUOm3/TeY0gXurgZdZmm4ZwZff+vPfBMPM3pb86mV54jaDqMbw9aZ8MgH0GGSLX/RasCr9Nn2K0ztBvdUh84zrHa3KnMUKA0thsHh1bD8S7urUQk2TYHRTeDaeejyKzR4C3zsiVoNeJV2W2fC1O7WKYJOGu62qPosVHseln8FB/6yu5rs7Ua01cN/Rm/rgOeFFdb1EhtpwKu02TIdpvWE4rWh03QIymN3RdnXE19C/lJWsFw9a3c12dO5AzC2GUSMhQffsGaQeUBLDg145bpNU2F6LyhRFzpOg8DcdleUvQXmhjZhVp+a317TVaAy2875MLIhnD0A7cOh6cfgm6b5KxlOA165ZuNkmNkHSj4IHadmylxe5YSiNeCR/7MueEf+aHc12UNCo7Dw9tZfUH3/gEpP2F3VbTTglfM2hMPMvlDqIXh+CgQE212RulW9V6FMY1gwAKJ22F2Nd7t0Aia0tBqF1ewOPRZ55ILxGvDKOet/tjoZlnkYOkyGgJx2V6QS8/GBZ0Zav3in9bAu+qmMt3+F1Sjs2Hprfz/1H/APsruqJGnAq9Stmwi/vgxlG1vzeTXcPVfuwtDqB4jaCos/sLsa7xIfDyv+bR25B+WF3v+Dau3tripFGvAqZZE/wuxXoNwj1gUk/xypbqJsVqEZ1HkR1oy0LgCq9Lt61jrX/vsgqNwK+iyFu+61u6pUacCr5EWMhd9eh/LNoN3PHvtnqEpC04+tthGzXoKLx+2uJms7us5qFLb3f/DE19biK1lk5pgGvEramtHWTRsVHoN2P2m4ZzV+gdBmLMRGWxfGdRUo1xkDa8fA2OaAgR4LoXbvTGsUlhE04NWdVo+CeW9DxSfguQlWWKisJ6QCPPY57P8D/v7G7mqylpjL1o1jc9+C0g9D3+VQrKbdVbnMM2bjK8+x6gdY0B8qtYC248AvwO6KVHrU6AJ7f4f/DYZSDbNkSGW6qB1W87wzu6HJ+/CQfb1k0itrVq3cY+V3Vrjf+xQ8+6OGuzcQgae+gdx3w/SeEH3R7oo826apVhfIa2eh8yxo2C/LhjtowKsEf30LC9+Dyk9bR+6+/nZXpDJKjvzWws7nD1qn3tSdYmNgzpswo5fV9rrvCuuejywu1VM0IpIXmIS1XN8VYBnwjOPL+YDVxpi+SWznB+xzfAC8aozZnP6SVYb78z+w5EOo8owVBBru3qdkPXj4XVj2GZR9BKq1s7siz3HuIEztat24VP81q3+7l/wMOHMOviMw1BizWER+APYbYxoBiMhwYHwy21UFwo0xunCkJ1vxb2tu731trbvyPKRJknKDBm9ba4LOfROK14ICZeyuyH47F1izjIyxpgLf28LuijKUM2uyfm+MWex4GgJEAYhIUaCwMSYimU3rAi1EZI2IhDmO6JUn+eMrK9zvf07DPTvw9bP+QvPxtVo9x163uyL7xMXCko8hvB3kKwF9l3lduIMLs2hEpB6Q3xizyvGpl4EfUthkLfCoMea4iEwAngBmJ3rNPkAfgBIlSrhS9z+OrbfutvTxBx8/65v45uNE/9722N8xNuGxv/WNf/Ox43mSj5N6H9+01W+XZV/Ask+hanto9X3Wq1+lTb7i8NS31imJpUOsG6Kym0snrV49B/+Emt3gsS+89j4PpwJeRAoAw4E2juc+QGNgYAqbbTLGxDgeRwDlEw8wxowCRgGEhoamrYn1pROwYx7E34D4OIi74Xic2WtUSgq/PJJ7nPiXyq2/hFL7peKXzHZOvOfO+fDnUHigI7QcruGe3VRpBXu7wl/fWP2FyjSyu6LMc+BPK9yjL0KrEfBAB7srcitnLrIGAFOBAcaYg45PN8C6uJpSKE8UkSHAFqAV8Gk6a01axceh3+47P2+MFfgJYR+X6N/bHt+w/mS7+fjGP9veMT6JXyRxscmMTe59btkuNgbiryQ9Prn6TAbclVi9Ezw1PEtPAVPp8NhncGglzOgLL/4FwYXsrsi94uOtm71+H2Rde+g8EwpXsbsqt3PmCL4nUAMYKCIDsU7LVAOWJwwQkcrA88aY92/ZbhDwCyDAbGPMkgyr2hki1pGrN55Xjo9P4ZdRCr+AEh77B0GJ+hru2VlAsNVTZXQTq1Noh0lZ6hZ8l1w7BzNfgF0LrJliLYdnmV4y6SUpH4RnntDQUBMRkdz1WqWUWyTcufz4l1DnjtnOWd/Rddb1hovHofkQqN3H636RiUikMSY0qa/pIZxS2VmdF6B8c1j0f3Bii93VZBxjYG2Y1SgsPh56LLB+gXlZuKdGA16p7EzEmkWVI5918fH6VbsrSr/rV2BGH2u+f+mG8MIKKJbkAa7X04BXKrsLLgTPjIDTO612FVnZqZ3WdYXNU6Hx+/D8VMhZwO6qbKMBr5SCsk2s2/Qjx8G22amP90Sbp8GoxnDltDVL5uGs3SgsI2Tv/3ql1D+a/B/cUx1mvwoXjthdjfNiY6y+7dN7WqtYvbDCmt+vNOCVUg5+AdAmzJpOO6OPNd3W0507CGMfs1ZeqvcKdJsDee6xuyqPoQGvlPpHwbLWuqMH/7Ia0XmyXQthZEM4s8daVrL5EK/pAplRNOCVUrer1h7ufxaWfQ6HVqU+PrPFxVp3pP7ynNVbp88ya5EadQcNeKXU7UTgyaGQtxhM7wXXzttd0T8unYSJray/Lmp0gZ6Lrb86VJI04JVSdwrKY7UyuHQc5rxh3ThktwN/WadkjkRAqx+slgP+OeyuyqNpwCulklYsFBq/B1tnwvqf7KvDGGvVsfFPWT10ev8ODzxvXz1ZiBd24lJKZZgH34C9S2H+O1CiLhS6o+u3e107B7Negp3zrPWCW/7X+utCOUWP4JVSyfPxhdajwC8IpnW35pxnlmMbYOTDsHuRtSjHs+M13F2kAa+USlmee+Dp7+DEZmuZO3czBiLGQVgza05+9/lQ94Vs1ygsI2jAK6VSV+kJqNUbVn0Hu924tMP1K9Yi2HPegFIPQt8VULy2+97Py2nAK6Wc0+wTuKsyzHrBmq6Y0U7tgtGPwKYp0Og96DgNggtm/PtkIxrwSinn+Oewpk7GXLJCPj4Dlo5MsHkajG4MV6Kg8wxo9K6uFZwBNOCVUs67616rJcDe/1mna9IrNgbm9bMahRWuYp2SKdsk/a+rACcCXkTyish8EVkkIjNFJEBEDonIMsfH/SlsGyYiK0Xk/eTGKKWymNCeUKmFdcH12Pq0v875QzDucVgzytEobC7kLZpxdSqnjuA7AkONMc2AE0B/INwY08jxsTmpjUSkNeBrjKkHlBGRTJ5Aq5RyCxHrLtLgEJjWE2Iuu/4auxdbd6We3g3PTdRGYW6SasAbY743xix2PA0BYoEWIrLGcYSe3M1SjYApjseLgIcSDxCRPiISISIRp06dcr16pZQ9chaw5sef3Qfz33V+u/g4+P0T+Lkt5ClqNQqr3NJtZWZ3Tp+DF5F6QH5gMfCoMaY24A88kcwmwcBRx+OzQOHEA4wxo4wxocaY0JCQEJcKV0rZrHQDaPAWbPjJukiamstRjkZhX0P1ztBriTYKczOnWhWISAFgONAGOGGMSbidLQJI7tTLZSChE1Au9IKuUt6nUX/Yvxzm/MvqXZO/VNLjDv4NU7tD9HnrpqnqnTKzymzLmYusAcBUYIAx5iAwUUSqiYgv0ArYmMymkfxzWqYacCDd1SqlPIuvP7QZYz2e3tvq1X4rY+Cvb+HHFhCQ0zpq13DPNM4cVfcEagADRWQZsBWYCGwAVhpjlohIZREZnGi7WUBnERkKPAfMzaiilVIeJH9JaDEMjqyBPz7/5/PXzsPkTrD4/6DSk9b59iLJTrpTbiDGjX2eRSQ/0BRYbow5kdLY0NBQExER4bZalFJuNusl2PALdP3Nago2pYu1eHfTT6Dui9pLxk1EJNIYE5rU19zaLtgYc45/ZtIopbzZ419aS/xN7Wbd7RpcCLrNgxJ17K4s29ILn0qpjBGYC9qGwfXLULI+9F2u4W4zXfBDKZVx7qkOb++CwDx6SsYDaMArpTJWUF67K1AOeopGKaW8lAa8Ukp5KQ14pZTyUhrwSinlpTTglVLKS2nAK6WUl9KAV0opL6UBr5RSXkoDXimlvJQGvFJKeSkNeKWU8lIa8EqlYE/UJZ4fvYqlO6LsLkUpl6XabExE8gKTAF/gCtAt0fN2xpjrSWznB+xzfAC8aozZnDFlK+V+u09eosPo1Zy+HMPaA2cZ3qE6j913t91lKeU0Z47gOwJDjTHNgBNA10TPH0tmu6pAuDGmkeNDw11lGbtOXqLD6FWIwMyX6lO1WD5e/mU9v244andpSjkt1YA3xnxvjFnseBoCrEn0PLm/XesCLURkjYiEOY7olfJ4O09cosOoVfiIMKlPXaqXyM+EHrWpVSo/b0zewJS1h+0uUSmnOH0OXkTqAfmNMauSep6EtcCjxpjagD/wRBKv2UdEIkQk4tSpU65Xr1QG23HiIh1Gr8LP1wr3siG5AAgO9GNct9o0KB/CO9M3MWHlAXsLVcoJTgW8iBQAhgM9knqejE3GmOOOxxFA+cQDjDGjjDGhxpjQkJAQlwpXKqNtO3aRDqNWEeDrw6Q+9SjjCPcEOQJ8Gd2lJk0rF+aDX7cyevm+ZF5JKc+QasCLSAAwFRhgjDmY+HkKm04UkWoi4gu0AjZmRMFKucPWYxfoOGYVQf6+TOpTl9KFgpMcF+jny/cda/Bk1bsZMm873/6+G2NMJlerlHOcOYLvCdQABorIMmDArc9FpJ2IVBaRwYm2GwRMBDYAK40xSzKubKUyzpajF+g4ZjU5HOFeKplwT+Dv68M37R6gdY2iDF28i68W7tSQVx5JPOUbMzQ01ERERNhdhspmEsI9V6Af4b3rUqJgTqe3jY83DJy1hfA1h+jxYGn+r8W9iC40rTKZiEQaY0KT+prObFHZ1qYj5+k0ZjW5g/yZ1KcuxQs4H+4APj7Cp8/cR6CfD2P/2k90bByDn74PHx8NeeUZNOBVtrTx8Hk6ha0mbw5/wnu7Hu4JRIQPn6pMjgBffli2l5gb8XzZtiq+GvLKA2jAq2xn/aFzdAlbQ75gK9yL5U9buCcQEd5pXpEc/r4MXbyLmNg4hrV7AH9f7QSi7KUBr7KVdYfO0TVsDfmDAwjvU5ei+XJkyOuKCK89Up5APx8+m7+DmNh4/vt8dQL9fDPk9ZVKCz3EUNlG5EHryL1ArgAmZWC436rvw2X5uGUVFm87SZ8JkUTfiMvw91DKWRrwKluIOHCWLmGrKeQI93vcEO4JutYvxRdt7mf57lN0H7eWKzGxbnsvpVKiAa+83toDZ+k6dg135QliUp963J3XfeGeoF2tEgx77gHWHDhLl7FruBh9w+3vqVRiGvDKq63Zb4V74TxBTOpTlyJ5gzLtvVtVL8p/O1S3ZuyMWc35q3d01VbKrTTglddate8M3catoUheK9wL58m8cE/w+P13M7JzTXYcv0T7Uas4fTkm02tQ2ZcGvPJKK/eeofu4tdztCPe7bAj3BI/cW5iwbqEcOHOFdiNXcvJitG21qOxFA155nb/3nKb7j2solj8Hk/rU467c9oV7ggblQxjfvTYnLkTz3MiVHDl31e6SVDagAa+8yl97TtNj/FpKFMhJeJ+6hOQOtLukm+qUKcjEXnU4e+U67Uau4uCZK3aXpLycBrzyGit2n6LHj2spVTCY8N51KZTLc8I9QY0S+QnvXZer12N5dsRK9kRdtrsk5cU04JVX+GPXKXqOj6B0oWB+7lWHgh4Y7gnuK5qXSX3qEW+g3ciVbD9+0e6SlJfSgFdZ3rKdUfSeEEHZkFz80ruuR4d7gopFcjOlb138fX3oMHoVm46ct7sk5YU04FWWtnRHFH0mRFIuJBe/9KpDgeAAu0tyWpmQXEzpW49cgX50HL2ayINn7S5JeRkNeJVl/W/HSfpOjKR84Vz80rsO+bNQuCcoUTAnU/rWo1DuQDqHrWHl3jN2l6S8iAa8ypKWbLPCvWKR3Pzcqw75cma9cE9wT74cTHY0P+s2bg1/7Dpld0nKSziz6HZeEZkvIotEZKaIBIhImIisFJH3U9nWqXFKuWLxtpO8+HMk996dh596Zu1wT3CXo5VC2ZBc9B4fwaKtJ+wuSXkBZ47gOwJDjTHNgBNAe8DXGFMPKCMi5ZPaSERaOzNOKVcs3HqCl36OpPI9eZnYsw55c/rbXVKGKZgrkPDedbn3njy89PM65mw6ZndJKotLNeCNMd8bYxY7noYAnYApjueLgIeS2bRRauNEpI+IRIhIxKlT+mepStmCLSd4+ed1VLknLxN71iZvDu8J9wR5c/rzU8/aVC+Rj9fC1zM98ojdJakszOlz8CJSD8gPHAaOOj59FiiczCbBqY0zxowyxoQaY0JDQkKcLlplP/M3H+eVX9ZRtZgV7nmCvC/cE+QO8md8j9rUK1uQt6dt5JfVh+wuSWVRTgW8iBQAhgM9gMtAQkPtXCm8hrPjlErR3E3HeSV8PdWK52N8j9rk9uJwT5AzwI+wrrVoXPEu3pu5mbF/7re7JOUmV6/Hcu26e1b+cuYiawAwFRhgjDkIRPLP6ZZqwIFkNnV2nFLJ+m3jMV6btJ7q2SjcEwT5+zKiU00eq1KEQXO28f2yPXaXpDJQbFw8k9YcotFXyxi9Yp9b3sOZRbd7AjWAgSIyEBgHdBaRe4DHgboiUhl43hhz62yZWcCKW8dlaOXK6/264Sj/mryB0JIFGNu9FrkCs98a8QF+Pvz3+eq8NXUjXy7YSfSNeP71aHlExO7SVBoZY/h9exSfL9jBnqjL1CiRjwfLFXTLe4kxxvWNRPIDTYHlxphk53M5Ow4gNDTUREREuFyL8k43w71UAcZ1q0VwNgz3W8XFGwbM2MSUiCP0bViG/o9X0pDPgtYfOsdn83aw5sBZyhQK5p3HKtG8SuF0/b8UkUhjTGhSX0vTT40x5hz/zJBJ9zilbjVz/RHemrKR2qULMLZbLXIGZO9wB/D1ET5vXZVAP19GLt9H9I04PnyqCj4+GvJZwf7TV/hq4Q7mbT5BoVyBDG51H+1qFcff172XJvUnR3mU6ZFHeHvaRuqWLkhYt1AN91v4+AiDnq5CkL8Po1fsJyY2niHP3I+vhrzHOn05hm9/380vqw8R4OfDG4+Wp3eDMpn2F6n+9CiPMS3yCP2mbaRemYKEda1FjgBfu0vyOCLCe0/cSw5/X7793x6ib8Tx9bPV8HPzkaByzdXrsYxZsZ+Rf+wlOjae9rWK8/qj5TN9dTENeOURpkQc5t3pm3iwbCFGdwnVcE+BiPBms4oE+vvy1cKdxMTG80376gT4acjbLTYunikRR/jPkl1EXYqheZXCvPNYJcqG5LKlHg14ZbvJaw/Rf8ZmHipnhXuQv4a7M15uXI4gf18+mbON6z9F8l3HGrrvbGKMYfG2k3yxYAd7T10htGR+fuhUg5olC9halwa8slX4mkMMmLGZhyuEMLJzTQ0oF/V8qDSBfj68P2sLvcZHMKpLTb1ukcnWHTrHZ/O2s/bAOcqEBDOyc02aVU7fzJiMot8Jyja/rD7EezM306hiCCM6abinVae6JQny9+WdaRvpNnZttr1nILPtO3WZrxbuZP4Wa2bMkGfuo11ocY+6HqLfBcoWP606yPuzttCk0l380KkGgX4a7unRtmYxAv18eGPyBjqNWc34Ht7ZjM0TnLpkzYwJX2PNjPnXoxXo1aC0R96r4XkVKa83ceUB/u/XrTxS6S6+13DPME9Vu4cAPx9e+WUdz49excSeWWsJQ093JcaaGTNq+V5iYuPpULsErz1SnpDcnrsGcJruZHUHvZM1exj/9wE+nL2VR+8tzHcdq2u4u8GynVH0nRhJyYI5+alXnUyfmudtYuPimRxxmGGLd3P6cgyP31eEfs0rUsammTGJpXQnq+ecLFJeb+yf+/lw9laaVi7M9x31yN1dGlW8i3HdanHk3DXaj1zF8QvX7C4pSzLGsHDrCZr9ZzkDZ26hdKGcTH+xPj90qukx4Z4aDXiVKcas2MegOdtoXqUw3z1fQ+dsu1n9coWY0KM2UZdieG7kSg6fvWp3SVlK5MFzPDtiJX0nRiLAqM41mdK3HjVL5re7NJfoT5lyuzEr9jF47nYev68I/9VwzzShpQrwc686XLwWy3MjV7Lv1GW7S/J4e09d5oWJkbT54W8Onr3Kp8/cz8I3GtKsShGPmPboKj0Hr9xq1PK9fDpvB0/efzf/af+A25srqTttO3aRzmGr8fERfu5VhwqFc9tdkseJuhTNN0t2M2ntYYL8fOj7cFl6NSidJe4pSOkcvAa8cpsRf+zl8/k7aFH1bv7T7gGPmh+c3eyJusTzo1cTG2+Y0KM29xXNa3dJHuFKTCyjV+xj1PJ9XI+N5/k61syYQrk8d2ZMYhrwKtN9v2wPXy7YyVPV7mHYc9oMyxMcOH2FjmNWcyn6BuN71KZ6iax1Pjkj3YiLZ/Law/xniTUz5on7i9CveSVKFwq2uzSX6Swalam+W2qF+9MPaLh7klKFgpncty75cgbQOWwNa/aftbukTGeMYcGWEzQftpz3Z22hTKFgZrxUn+871syS4Z4aZxfdLiwiKxyPXxSRZY6PDSIyMplt/ETk0C1j78/IwpVnGv77br5auJNnqhdl6HN6WsbTFMufkyl961E4TyBdx67hrz2n7S4p00QcOEvbESt54adIfHyEMV1Cmdy3LjW8+C+ZVE/ROJbdCwfuMsbUSPS14cB4Y8wd51ZEpAbQzhjzrjOF6CmarO+bJbsZtmQXrasX5atnq+lCFB7s1KUYOoetZt/pK4zsVJPGle6yuyS32RN1mS8X7GDRtpPclTuQN5tWoG3NYl5z8JHeUzRxQDvgYqIXLQoUTircHeoCLURkjYiEicgdl6NFpI+IRIhIxKlTp5woRXmqYYt3MWzJLtrUKKbhngWE5A4kvHddKhbOTZ+JESzYctzukjJc1KVo3pu5meb/Wc7fe8/wdrMKLOvXiPa1S3hNuKfG6YusIrLMGNPoluefAouNMUuTGV8LOGKMOS4iE4BpxpjZyb2+HsFnTcYYhi3exbf/28OzNYvxeZuqGu5ZyMXoG3Qbu4aNRy4w9LlqPP1AUbtLSrfLMbGMWr6PMSusmTGd6pbk1SblKJiFZsa4IsMX3RYRH6AxMDCFYZuMMTGOxxFA+bS8l/Jcxhj+vWgX/126h3ahxfms9f26CHQWkyfIn4k969Bz/FremLyBmBvxPFeruN1lpcmNuHgmrTnEN7/v5vTl6zxZ9W76NatIKS+8eOqstM7ibwCsNikf/k8UkSHAFqAV8Gka30t5IGMMXy3cyffL9tKhdnGGtNJwz6qCA/0Y1602fX+K5J3pm4iOjaNLvVJ2l+W0hJkxXy7cyf7TV6hTugBjut7LA8Xz2V2a7dIa8M2B5QlPRKQy8Lwx5v1bxgwCfgEEmG2MWZLmKpVHMcbwxYKdjPhjL8/XKcHgp+/TcM/icgT4MrpLTV7+eT0f/LqVmBvx9G5Yxu6yUrX2wFk+m7eddYfOU/6uXIR1DaVJpbuyZFsBd9AbnZRLjDF8Pn8HI5fvo1PdEgxqqeHuTW7ExfPGpA3M3Xyct5pW4NVHPPPM6p6oS3yxYCeLt52kcB5rZkybGt4zM8YVGX4OXmVPxhg+nbed0Sv206VeST5uWUWPlLyMv68P37R/gEB/H/69eBfRsXG83ayix/x/jroYzbAlu5m89hA5A/zo17wiPR4sTY4AbT2dFA145RRjDIPnbifsz/10q1+KD5+q7DE/9Cpj+fn68HXbagT6+fLd0r1cux7P/7W419b/35djYhn1x15Gr9hPbHw8XeqV8uqZMRlFA16lyhjDoDnbGPfXAbo/WIoPWmi4ezsfH+HTZ+4j0M+HsX/tJyY2jk9suNZyIy6e8DWH+GbJbs5cuU6LqnfTr3lFShbMvjNjXKEBr1JkjOHj37bx498H6PFgaduP5FTmERE+fKoyOQJ8+WHZXqJvxPNl28y5z8EYw/wtJ/hywQ4OnLlK3TIFGPv4vVTTmTEu0YBXyTLG8OHsrUxYeZBeD5Vm4JMa7tmNiPBO84oE+fkybMkuYmLjGNbOvX39V+87w2fzd7Dh8HkqFM7FuG61aFQxRL/30kADXiUpPt7wwewt/LTqEH0blqH/45X0ByybEhFef7Q8Qf4+fDZ/B9dj4xn+fMYvmL4n6hKfz9/Jku0nKZIniC/bVqVNjWJ6Z3Q6aMCrO8THG/7v1y38vPoQLzxclncf85xZFMo+fR8uS5C/Lx/O3kqfCZGM7FyTIP/0h/zJi9H8Z8kuJq89THCAH+88VpHu9XVmTEbQgFe3iY83DJy1mfA1h3mpUVn6NddwV//oWr8UgX4+DJi5me7j1jKmayjBgWmLkUvRNxj5xz7G/LmPuHhDt/qleaVJOQoEB2Rw1dmXBry6KT7eMGDGZiZHHOaVxuV4q1kFDXd1h/a1SxDk78tbUzfSdewaxnavRZ4gf6e3vx4bzy+rD/Lt//Zw9sp1Wla7h7ebVaREwZxurDp70oBXgBXu707fxNTII7zWpBz/aqrhrpLXqnpRAv18eDV8PZ3GrGZCj9rky5nykbcxhnmbT/Dlwh0cPHOVemUKMuCJSlQtli9zis6GNOAVcY5wnxZ5hNcfKc+/mlawuySVBTx+/92M9PPhxZ/W0X7UKn7qVSfZxapXOWbGbDx8nkpFcjOuey0aVdCZMe6W/Ro3qNvExRv6TdvItMgjvPGohrtyzSP3FiasWygHzlyh/ahVnLwYfdvXd528RM8f19J+1CqiLkbzVduqzH2tAY0rakOwzKDNxrKxuHjD21M3MnP9Ud5sWoHXPLSxlPJ8q/edocePaymUO5BfetfFV4Rhi3cxNfIwwYF+vNSoHN0fLJUhs27U7VJqNqYBn03FxsXz1tSN/LrhGG83q8ArTTTcVfqsO3SOrmPXEOTvy6XoG8THQ+d6JXmlcTny68wYt9Fukuo2sXHxvDllI7M3HqNf84q83Lic3SUpL1CjRH7Ce9flhZ8iqV+lCG83q0jxAjozxk4a8F4iPt4QExtP9I24VP+dt/k487ecoP/jlXjh4bJ2l668yH1F8/Lnu03sLkM5aMBnMFeCNuHfhMcxLmxj/fvPNtfj4l2q870nKtGnoYa7Ut7MqYAXkcLANGNMAxHxA/Y5PgBeNcZsTma7MKAyMNcYMzgjCnaWM0Gb3NdcCdroG/G3beNq0CYW5O9DoJ9vkv/mDPCjQLD1PDCJrwf5+xLol/q/eXP6c1fuoAza00opT5VqwItIfmA8kNCAuSoQbox5N5XtWgO+xph6IjJWRMobY3anu+JElu2M4pM52zI8aBMHoytB62rgBib86+ejU8eUUhnGmSP4OKAd8KvjeV2ghYg0BjYDfY0xsUls1wiY4ni8CHgIuC3gRaQP0AegRIkSrtYOQJ4c/lQqkifFoHU2eDVolVLeJNWAN8ZcBG4NvLXAo8aY4yIyAXgCmJ3EpsHAUcfjs0CNJF57FDAKrGmSrhYP1pX7Gh3zp2VTpZTyamm5yLrJGBPjeBwBJDeB+jKQw/E4F3rXrFJKZaq0hO5EEakmIr5AK2BjMuMisU7LAFQDDqThvZRSSqVRWo7gBwG/AALMNsYsEZHKwPPGmPdvGTcLWCEi9wCPY527V0oplUnc2qrAMQOnKbDcGHMipbHaqkAppVxnW6sCY8w5/plJo5RSKhPphU+llPJSGvBKKeWlNOCVUspLeUw/eBE5BRxM4+aFgNMZWE52oPvMNbq/XKP7yzXp2V8ljTEhSX3BYwI+PUQkIrmryCppus9co/vLNbq/XOOu/aWnaJRSyktpwCullJfyloAfZXcBWZDuM9fo/nKN7i/XuGV/ecU5eKWUUnfyliN4pZRSiWjAK6WUl/L4gBeRvCIyX0QWichMEQkQkTARWSki798yrrCIrLjl+YsisszxsUFERtrzX5C50rG/8ovIPBGJyC77CtK1v0qLyFwRWSEi/7an+sznzP5Kaozj83fsV2+Xzv112/dcWnh8wAMdgaHGmGbACaA9jrVegTIiUj6JdWMxxvxgjGlkjGkErABGZ37ptkjT/gI6Az875uLmFpHsMoc5rfvrC+ATY0wDoJiINMrcsm2T6v5KYsxjt67RfMu47CCt+yup7zmXeXzAG2O+N8YsdjwNATpx51qvCevGXky8vYgUBQobY7JFL+J07K8zwH0ikg8oDhzOlIJtlo79VQFY53gcBeR1f7X2c2Z/JTEmiqTXaPZ66dhfyWaaKzw+4BOISD0gP1bw3LrWa2FjzEVjzIVkNn0Z+CETSvQoadhffwIlgdeA7Y6x2UYa9tc04EMReQp4DPg904r1ACntr8RjjDGruHON5sJkI67ur1QyzWlZIuBFpAAwHOiBC2u9iogP0BhY5uYSPUoa99eHwAvGmEHADqC7u+v0FGnZX8aYwcB8oBcw3hhzORNK9QjO7K9EY0huXHaQxv2VITx+JzsuOEwFBhhjDuLaWq8NgNUmG032T8f+yg/c71hrtw6QLfZZOr+/NgAlgKFuLNGjOLO/khhDUuMyrWgbpWN/ZQxjjEd/AC8C57COwpcBXbEW+h6KdSoh7y1jlyXa9lOgtd3/DVlhfwG1ga1YRxiLgVx2/7d48v5yPP8Y6Gz3f4On7a8kxrQD8iS3X735I637K7nvOVc/suSdrK6s9ap0f7lK95drnN1ful8tmbkfsmTAK6WUSp3Hn4NXSimVNhrwSinlpTTglVLKS2nAK6WUl9KAV0opL/X/1x4MdkiqnDQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mJjJixAjKlCnD3r17SUlJoXLlykRGRtKxY8eckZR//fUXmZmZuLi4yOyAomBM6f+0sBOioGoreGyB1lukJAf2v1UKhqe/gYt7tGH0G97QeqK0fxOC+4OxZPwu6/9d5tUyzkyFuEjITAZnTyhdtVCnh3zooYcAeOedd3IuKWTz8/NjwoQJgDbBU1JSEpUqVeLUqVMAOde4s/3+++8yO6CwjinjlsC+BFVbQs9PtP7YEth5q9YSBqyHiN9h6xT4aYTWNTJ0HAT2LvbzoOgf3HlxdoeydbSZzOKjIOYkeJbXbloU4uxizs7OOVO53iouLo7SpUsDcOrUKZYuXcqqVatQVTWnxZ2ens6XX35J27ZtZXZAkT+mDDj0lRbY8ZHg3wJ6/k8b8SiBfW8URRtxGdAB/v5Fm0r2uxe0G7kPjoe6nYvtubTf4AbtpHv4gauPdr0v+Zo26buPP7j5FMoPxWg0cv/9t3epmjp1Kj179mTbtm3Mnj2bFi1a0LJly5wg/XeLO5vMDijuypQB4Sth+yyIvwhVmkH3OdqcIsU0ZGxOUaB+Vy2oj36vzYPydT/wb64Now9or3eFhc6+gzub0QlKV9NCPC4SYs+Bq7cW4AW8KZGYmMhrr71222N///03FouFxo0b8+uvv+Ll5cXy5csZNWoUiqLktLhVVaV379689NJLMjuguDNzJhxaCTtmapMrVWkG3WZDbQnsQmMwQuMnILCXdq63TYcVPbT7BA9NBP9cu0Q7JMcbgKOq2iQ6iVe0r70qQqnyWid9GzKbzRgMBpRb/pOpqorFYsFoNMrsgCJ35kwI/1rrxhZ3ESo31eajlulMbS8zTZvHZftMbabEul20SygVG+ld2T0pXgNwFEULavfS2rXvxCuQcjPr8ontBpFkX8a4vRQl53GZHVDcxpwJ4auyAvuCtpbjo7O0uaklsIuGs5s2YOe+Z7U1MP+cBwvbajMThr4NZWvrXaHVHC+4sxldwLemtkp1/CW4eRbcSmsrTxslHIVOzCY4nBXYseezAvsjbakwCWx9uJbS+ns3f0HrOrh7IRxbC02e0roRlq6qd4X55rjBnc3NG1zqQ/JVSLwK6QngVQk8y8l/FFF0zCY4vDorsM9p/Y37r4a6j8jvob1wL6Nd6245XOvNs3+p9jMLGQgPjAWvCnpXeM8cP7hB67PpVUn7wcRf0gYwpNzU3klL6JBYUUTMJjjyLWyfATcjoGJj6Pc11OsigW2vSpXXxo+0GQHbZmjzgf/1pTaEvs1I8PC9+z50Vrx6qTu5gW8tKFMTLCa4fkq7IWTOX4+M3NzrTVyZ1rWEMJu0a9iftNAWwXXxhH4rYdh2qP+ohLYj8PGHHvNgxD6tO+Efc2BuE21VnvREvau7o+IV3KD9h3Evra2g4VleW9k65oT29y3he/nyZSpWrEhoaCjt2rWjf//+Oc+tXLmSpUuX3rbbrl27cuRIXnOqwLhx4wgNDaVOnToEBQXRsGFDAgMDCQ0NZciQITnbff755zRs2JCOHTvSoEEDAgMD6dixIyEhITmLD4M2rWu2uXPn5syhAvc2bP7ChQuMHTv2P49fvXo1Z9/jxo3j9OnTd92XuIXFDOGr4dOW8MMwcPaAvv8Hw3Zo//klsB2PXy1txsUXd2rT5P4+VQvwXZ9oPVPsUPG4VJIbg1G7Uenhq/X9jruohbePNnTeycmJzp0788EHH+Dm5saoUaMwm80YjUacnZ1JT0/P+feiRYvYsGFDzsRRsbGxmM1m9u7dm9MnW6Z1LeYsZjj6nfbR+sZpqNAI+n4F9boW++HVJUaFQOi/Ei4dgN+mwMa34c/50P4NuO8ZMDrrXWEO3YN7+t7pnLx5slD3Wd+3Pm+2eFP7Rx5D5w1Z3/rSpUupVasWBoOBAQMGcPHiRf78808CAgI4f/481atXZ8uWLXz99dds3ryZcePGMWrUKObOnZsT2iDTuhZbFrM2Gm/bdC2wywfCk19qayNKYBdP/iHw3Fo4t0ML8PWjtUWYO7ytdSUsxCk3rKV7cBeJXIbOqzcTwZyBi7MzpUuXRlVVvvzySw4ePMjjjz/OuHHjGDBgALGxsTzzzDMcOHCA8+fP06pVK6ZOnfqfialkWtdixmKGYz9oLezrf0P5hvDEcmjQQwK7pKj5AAzaCKc3aQH+/RCtN8qD72hv3DpeFtM9uHNaxkXhlqHzauwhSE/i5uUIPJo3xWw2A/Dee+8xcuRIQJtadeTIkZQtW5aQkBDatGnDtGnTuHTpEpMmTSIyMpJBgwbRo0cPmda1uLBY4HhWYMechHIN4IkvoEFPCeySSFG0Lp21O8GJH7W5wFc/o/XPf3CCNsmVDgFeMv/HunhirFCPoCYh7NuziwAvMyGBdfhoxgxatmxJ1apVSUpKon79+rz++utMnjyZy5cvc+rUKTZs2ECpUqUA+OCDD2jdurVM61ocWCxwfK12SSTmJJSrD32WQcNeEthC+x0IfAzqd9f6fod9CF/1hupttQAv4oWaS+xvpF/Zsjw9aBjx6SpT5i8nsEY5Du3ZzlujX7ptu9DQUH777TfWrFlDuXLl2Lt3L2FhYTRu3Din61/2tK5BQUGEhYXx7LPP0qNHD8LCwvD392fy5Mk515azp3XdsmULo0aN4vXXX2fLli23Lep767SuqqoSExPD5cuXMZlMjBo1ih07drBixYo8v7fsaV1vdfr0aWrVqoWrq6tM63ori0W7JLKgDawZqPU86vM5vPgnNCr+8zqLfDI6wX1Pwyv74dGZcOMMLOsMX/WBy4eKrIyS2eJG6w741FNPMWfOHFxdXRnx0nC2rlrAtTMH2L9jC0HNtHdQZ2dnwsPDGTx4MJMmTcJgMJCRkcHZs2dzVs7JbvnqOa2r2WImzZyGu5O7TOt6L8yZcHK91mf32jEoWxceX6q1quzg5pOwc06u0GIINHka9i7WFnFY3B4a9oQO70C5erY9vE33bsdu3rzJtGnTaNKkCQA/b9gILi7ERd3ECTPdWtTCkhBNz6eH4OPjw4IFC2jWrBkXL16kd+/etGnThho1agBaAOsxrauqqiRlJhGfHk9CRgKqquJkcKKse1mmTJ3C4fDDOceRaV3RWtNXDmkDZ46s0WaM86sDvT/Lal1LYIt8cvGA+0dDs4Gw61PYNR9OrIPG/SD0TShTwyaHdbxpXYuCKU0bOp+eiFlxwehX465D54tqWldVVUkzpRGXEUd8ejxmixmjYsTb1RsPZw9i02JJyUzByeCEn7sfZVzLYCzEQLL7n11u4i/B4W+0a5MxJ7VJyOp10f5z1X1EAlsUnuQbsHO2thK9xQwPTdBWpLeCXU7rmr06ul3KHjqfFo8x/pI2dN7DD7wq57kYqa2ndc0wZxCfHk9cehwZ5gwURcHLxQsfFx9KuZTCkDUfeWnX0iRnJhOTEsPV5KtcT72On5sfvm6+BQ5wh7qJmZ6ktXzCv4Zz2wFVW4C322ztcoh7Gb0rFMWRpx88PBVavaxNOFa2rk0Oo0twu7m5cePGDfz8/Ow3vLOHzrt6QWK0tmxaWjx4VwZ33yLpAmSymEjISCA+PZ6UzBQAPJw98HP3w9vFGydD7j8+T2dPPH08SclMISY1hmsp17iRegNfd1/83PysCnBVVblx4wZubm4F+p5symKGc9u0SyEn1kFmCpSurk3d2fhJbWizEEXBuxJ0+9hmu9cluP39/bl06RIxMTF6HN46ZgOk3gDTZe3GhLuvTYbAqqpKujmdFFMK6aZ0VLTr1u5O7rg7uZNmSCONNK5y9Z73aTKbiMuMI8oUhUExaMHu7JnTSr9Xbm5u+Pv75/dbsr2rx7U5sA9/oy2s4eqjBXVwf23VdHttHAhhJV2C29nZmZo1a+px6IKxWLSFXjdN0FrfrV+C9m9pE7UXgKqqhMeEs+7sOjZe2Eh8ejy+br48WvNRugV0o6Ffw0L5ZHLixgkWH17Mlotb8HDyoH/9/jwX+By+bvY/jeV/JF3TbjCGfw3Rh8HgpA2S6PyBtkSVsx1/MhCigHS5OenwUm7Clnfh4HLwrqKFRYMe+W7ZXUi4wPqI9aw/u55LSZdwM7rRoVoHugd0p1XlVjgbbDOpzanYUyw5vISN5zfi5uTGk3WfZECjAZR1L2uT4xWazFT4+xdtdr4zW0A1Q6UmWsu60eNQqpzeFQpRaO50c1KCuyAi98L6MXD1iNbae3QG+Abc8SU3027y67lf+TniZw5fP4yCQstKLekW0I2O1Tvi6Vx0Cz9ExEWw5MgSfjn3C84GZ56o+wQDAgdQwdOOVgKxWCByt9ayPrZWW+HIu4p2KaRxPyhfX+8KhbCJQg9uRVF8gFWAEUgG+qqqmpHbtsU6uEGbUH/vYvh9mrZ4wwNjte4/Tq45m6SZ0gi7FMb6s+vZGbUTk2qibpm6dA/oTpeaXXQPygsJF/jsyGesO7sOg2Kgd53evNDoBSqVqqRfUTfOajcZD6/SpuR19tQGNwT3hRoPSBc+UezZIrhfAk6rqrpZUZQFwAZVVX/KbdtiH9zZEi5r8/ce+wF8a2F5dAb7Pb1ZH7GezRc2k5SZRHn38nQN6ErXgK7U87XtyCprRCZGsvTIUn48+yMAPWv1ZHDQYPy9iuiGZMpNOPa9dink0l5AgYBQ7VJIg26yDJ0oUWx6qURRlDXATFVVd9/y2FBgKEC1atVCLly4UKBjOJIzh79i3a4Z/OyUyVUnJzyc3OlU/WG61epG8wrNC3UwjK1cSbrC0qNL+f7091hUC90CujGk8RCqe1cv/IOZMuDMZu1SyKmNYM7QZuRr0h+CntC6XwpRAtksuBVFaQ1MVVX1oby2KQkt7piUGH459wvrI9Zz8uZJjIqRNm4V6B55nNAMFfcO70DzIXkO3rFXV5Ov8sWxL/j21LdkWjLpUrMLQ4OGElD6ztfx70pVIeqgdhnkyBptkQvPchD0pHYppGJj6cInSjybBLeiKL7AJuBxVVXzbFIX1+BOyUxh68WtrI9Yz+4ru7GoFhr5NaJbrW50rtEZP3c/7TrtL6/D2a1QMQi6zoaqzfUuPd+up15n+bHlrP57NWmmNB6p8QhDGw+lTpk6+dtRXKQ27Dx8lbaajJMb1HtUuxRS60GHe2MTwpZscY3bBdgAfKiq6uY7bVucgttkMbHnyh7WRazjt4u/kWpKpUqpKjnXrQN8cmmJqiqc+Ak2vKWtvtP0eej4rrYWpoO5mXaTL49/ycoTK0kxpdCxWkeGNh5KA787zF2SlqB9/+Gr4PwO7bHqbSG4n3az0c2naIoXwsHYIrhfBN4HwrMeWqCq6urctnX04FZVlRM3T7A+Yj0bzm3geup1vFy8eKTGI3QP6E6T8k3ubQRieqI2+fruBdpQ+k6TIfgph5zvOT49PifAEzMTCfUPZVjwMBqVbaRtYDZBRJh2KeTEejClat0kg/tr3fhsNGOaEMWJ9OO2wpWkK/x87mfWn13P2fizOBmcaO/fnm4B3Wjn3w4Xo5Uz/EUfhZ/HQOQeqNYaus7SVpd2QAkZCaw8sZIvj39JQkYCbcsGM9ziRZO/t0LSVXArrQ2MCe4P/s3kurUQ+SDBfY8SMxLZfGEz6yPWsy96HwD3lb+PbgHdeKTGI/i4FtLHehsNnddFYjRJh/6PVSe/ZoUxhVijkZZKKYbX60ezkOG39WcXQtw7Ce47yDRnsvPyTtadXUdYZBgZlgyqe1enW0A3ugZ0papXVdsdvJCGzhe5jJSsoedfw9nfQLVAlRBSgvrwrSssO/UNN9Ju0KxCM4YFD6NlxZb2OwukEHZKgvtfVFXl8PXDrD+7nl/P/0pcehxlXMvQpWYXugV0o1HZRkUbNFYMnS9yFgtc2KndZDz+I2Qkgk9VaNxXu9FY9p8eJqmmVL479R3Lji7jWuo1mpRrwrDgYbSt3FYCXIh7JMGdJTIhUpvUKWI9FxMv4mp0pUPVDnSv1Z3WlVvbbFKne3IPQ+d1cf101tDz1RAfCS5eENhTmyekets73lxNN6fzw+kfWHp0KdHJ0TTya8Tw4OG0828nAS7EXZTo4I5Li2Pj+Y2si1hHeEw4CgotKraga0BXOlXvRCkXO7uu/K+h83SdqfVxLkrJN7KGnn8NUQdAMWg1BPfX+l27eORrd5nmTH48+yOfHfmMqKQoGvg2YFjjYXSo1iHfc4ILUVKUuOBON6ezLXIb6yLW8UfUH5gsJmqXrk33Wt15tOajVPSsWKT1WOXMVvjlNbgZAYG94ZH3tVU1bMWUrg05D18FpzeBJRMqBGmXQYL6gFfBz1mmJZOfI35myeElXEy8SO3StRkWPIxO1To5xFQAQhSlEhHcFtXCwasHWR+xnk3nN5GYmUg593I8WvNRutfqTt0ydR3v43lmGuycCztmaQvcPljIQ+dVFS7t08L66HeQFgelKmhzhAT300Z72oDJYuLX87+y+PBizsWfo6ZPTYY2HkrnGp3zXI5NiJKmWAd3RFwE6yPW83PEz1xOvoy7kzudqneia0BXWlZsWTxacoU9dD72vLbMV/jXWoveyV2bfS+4H9QMLbKh52aLmc0XNrPo8CLOxJ2hund1BgcNpmtAV33vNwhhB4pdcF9Pvc6GcxtYH7Ge4zeOY1AMtK7cmu4B3elQtQMezvm7BusQbhs6fwVCnoeHJt370Pm0eG0hgsOrtd4hoM1rHdwfGnQHN2+blX43FtXCbxd/Y9HhRZy8eZIqpaowJGgIPWr1wNkG63oK4QiKRXCnZKbwe+TvrItYx+7LuzGrZhr6NaRbQDe61Oxi/8tuFZb8DJ03m7R+1uFfa/2uTWngV0drWTd+EkpXK/Ly70RVVbZd2sai8EUcvXGUSp6VeKHRCzxW5zHrR6oK4aAcNrjNFjN7ovfwc8TPbLmwhRRTCpU8K+UMjqlVulYhV+tA8ho6r6ra4rnhq+DIt5Aco61IH9RHC+zKTe1+gI+qquy8vJOF4QsJjwmnvHt5BgUN4vE6j+PmJIsAi5LBIYN764XfmbpnCtdTY/By9uLhGg/TLaAbTSs0lS5k2f49dL7xk3AlHK4d125m1u2sXQqp3RGcHK/Fqqoqe6L3sDB8IQeuHsDPzY+BjQbyRN0niuflMCFu4ZDBvWL/bj7Y/TG+amsal2lNYGU/6lf0pkFlbyr7uDleDxFbyhk6vwL8m2st68DHHHLq2Lzsi97HosOL2HNlD2Vcy/Bc4HP0r9+/SBdXFqIoOWRwn7mWyLrwK5y4ksDJ6EQu3kzJec7bzYn6lbxpWMmb+hW9aFDJm7oVvHB3KQY9SArCbCr2ixEcunaIhYcXsjNqJz6uPjzb4FmeavAUXi5eepcmRKFyyOD+t8S0TE5dTeT4lUROXkngxJUE/o5OJDnDDIBBgRplPWlQ0ZsGlbykdV7MHYk5wuLDiwm7FIaXsxdPN3yaZxo8U3gzOAqhs2IR3LmxWFQiY1M4cSUxq2WewIkrubfOG2S1zOtX8qaetM6LjRM3TrDo8CK2XtyKp7Mn/ev357mGz1HGrYzepQlRIMU2uPOSlG7i7+iEPFvnigI1/Ty1IM8JdC+qlHaX1rmDOhV7isWHF7Pp/CbcnNzoW68vzwc+X3K6iYpip8QFd27upXXu5eb0z6WWSt40kNa5w4mIi2DxkcVsOLcBZ4MzT9R9goGNBlLeo7zepQmRLxLcd5DdOv8n0LVWurTOHduFhAssObyE9RHrMSpGetfpzQtBLzjGBGNCIMGdbxaLyqXYVI7ntMy1QL9w486t87oVSuHhUrx7dTiayMRIlh5Zyo9nfwSgV+1evNDoBfy9/HWuTBR3FtVSoDEnEtyFRGud336pJbfWef1KXjSo6J0V6NI6twdXkq6w9OhSvj/9PRbVQvda3RkSNIRq3vY17F8UDyaLiYG/DqRbQDf61u9r1T4kuG1IWueO5WryVZYdW8aaU2swWUzMDp1Nh2od9C5LFDNfHf+K6fumM6v9LB6u8bBV+5Dg1oG0zu3b9dTrDN88nNi0WH7s9aP9rYQkHFZ0cjQ91/akaYWmfPrQp1b/f5bgthPZrfMT2S3zK4mciE7ItXVev5JXzg3RehW9pHVuA0dijvD0L0/zVIOneKvFW3qXI4qJMWFj2H5pO2t7ri3QvZQ7BbekQREyGBSq+XlQzc+DRwL/6d2QnG7SerPcEujfH4wiKf0CoLXOa/h5/jMitIS0zi0WFZNFxWxRMVksWX+r//xtzuNxiwWTWc398ex/m1UMhjI8VvsJVp5YSfeA7gSWDdT7WxYObvul7Wy+sJlRTUfZ9Aa4tLjtlMWiEhWnXTu/19Z57fKlcDIoVgdZro/f9nwuj+e5f0sur88raFVM5v8+XhTKeVtwrj4Tf+8KrOy6UpZOE1ZLNaXSa20v3J3c+bb7twVeBERa3A7IYFCo6utBVd/8tc4LtQYFnAwGjAYFJ4OC0Zj1t0G5/fHsx4wKRoMh5zFnowE35+xtDLns4/btb91fznPGPB6/7Zi5PJ5HTbfWH52QxuT1xzh5vjOJ/itZcHA5rzR7odDPoygZFoYv5HLyZb7o/IXNV26S4HYwnq5OhFQvQ0j1f+biyG6dn41JQoU8w9XJeIeAMxhuC1WjomAwFN/LMADV/DxY+1Jblv9ZhdlHDrLoyKekxgXyaocWOBtlzndx707HnmbFsRU8VvsxQiqE2Px4cqlECODg5TMM3NyX9MTaVM98mfd7NyKkevGZz1zYjkW1MPDXgUTER/BTr58KbYKzO10qkWaFEEDTyrUZ2fQlnL2Oc5ODPL5gF+O+P0J8SqbepQk7t/bMWg5eO8iYkDFFNiulBLcQWZ4LfI7apWvj4/8zz7etxDf7I3no4zB+PBSFrT+ZCsd0M+0mHx/4mJAKIfSq3avIjivBLUQWZ4Mzk1pP4mpKNKUqbuWnEW2pUsaDUasO8dznezl/PVnvEoWdmbV/FsmZyUxoNaFIu+ZKcAtxiyblm9Cnbh++OvEVBtfLfP9iGyb3DOTQxTgenrOd+b+dJsNk0btMYQf2Re/jp7M/MTBwILVK1yrSY0twC/Evo5uOprRraSbvmgxYeK51DbaMbU+nBhWYuekUj87bwZ6IG3qXKXSUYc5gyu4pVClVhSGNhxT58SW4hfgXH1cf3mj+BkdvHOWbU98AUMHbjU+ebsqyAc1JyzTTd/Fu3lgTTmxyhs7VCj18cewLzsWfY3yr8bg7uRf58SW4hcjFozUfpXWl1sw9OJdrKddyHu9QvzybX23P8Pa1+P5gFA99vI3vDlySm5clSGRCJIsPL+bh6g9zf5X7dalBgluIXCiKwvhW48k0ZzJ97/TbnnN3MfJWl/qsH3k/Nfw8GPttOE8t2cPZmCSdqhVFRVVVpu2ZhpPBiTdbvKlbHRLcQuShmnc1hgUPY9OFTWy/tP0/z9ev6M2a4W2Y9lgjjl2Op8ucHczefIq0TLMO1YqisPHCRnZe3skr972i6zqmMnJSiDvINGfSZ10f0kxp/NDzBzycPXLdLiYxnak/H+fHQ5cJKOvJ1Mca0aaWrDBfnCRmJNJjbQ/Ke5Rn5aMrMRpsu4i4TUZOKopSQVGUHdaXJYT9czY6M6HVBC4nX2bh4YV5blfOy5W5/e5jxaAWmFWVp5bsYcw3h7iRlF6E1Qpb+t9f/+Nm2k0mtp5o89C+G6uCW1GUMsBywLNwyxHC/jSr2IzHaj/Gl8e+5FTsqTtu265uOTaObseIDrVZF36Zhz7exup9F7EU0TS1wjaOXj/KqpOr6FevH4F++s/bbm2L2wz0BRIKsRYh7NaYkDF4uXjx3q73sKh3HoDj5mzktUfq8cvIB6hb3os3vztCv8W7OX01sYiqFYXJZDExeddkyrqX5ZX7XtG7HMDK4FZVNUFV1fi8nlcUZaiiKPsVRdkfExNjfXVC2InSbqV5rflrHI45zJpTa+7pNXUqeLFqaCtmPN6YU9cSeXTeDmZu/FtuXjqY1X+v5sTNE7zZ4k27WZvUJr1KVFVdrKpqM1VVm5UrV84WhxCiyHUP6E6Lii2Yc2AO11Ov39NrDAaFJ5tXZeuY9nQPrsz838/wyJztbD8lDRpHcDX5Kv/763+0rdKWh6tbt1q7LUh3QCHuUXbf7jRzGjP2zcjXa/1KufLxk01YObglRkXhuc/3MvLrv7iWmGajakVhmL5vOiaLiXdavmNX67tKcAuRDzV9ajI4aDAbzm3gz6g/8/36NrXL8suoBxjdsQ6/Ho2m46xt/N+eC3Lz0g5lL/w7rPEwqnpV1buc20g/biHyKd2cTp+f+mCymPih5w+4OblZtZ+zMUmM/+EouyJu0LRaad7vHUT9it6FXK2wRqoplcd+fAxXoytruq+x+RqSuZEVcIQoRK5GVya0msClpEssPrzY6v3UKleKlUNaMuuJYM7fSKHbvD/4YMMJUjJMhVitsMai8EVEJUUxodUEXUL7biS4hbBCi0ot6FGrB8uOLeNs3Fmr96MoCo+H+LN1THt6N63Com0RPDx7O7+fvHb3FwubOBN7huXHltOzVk+aVcy1was7CW4hrDS22Vg8nT2ZvGvyXft2300ZTxdm9Alm9dBWuDkbGfjFPl7+v4NcTZCbl0XJolqYsnsKni6ejG02Vu9y8iTBLYSVfN18GRsyloPXDrL2zNpC2WfLAD9+GfkArz1cl80nrtJx1jZW7DqPWW5eFokfz/zIwWsHGRsytsgW/rWGBLcQBdCrdi+alm/KrP2zuJFaOKviuDgZGPFgHTaNbkeTaqWZ+OMxen+6k2OX8xzzJgpBbFossw7Momn5pvSs3VPvcu5IgluIAlAUhYmtJ5JiSmHW/lmFuu8aZT1ZMagFc/s1ISoulR7zdzJ1/XGS0+XmpS3M2j+L5Axt4V+DYt/RaN/VCeEAapWuxcDAgayLWMeeK3sKdd+KotCzSRW2jgmlb/OqfPbHOTp9vI3Nx68W6nFKun3R+/jx7I8MaDSA2mVq613OXUk/biEKQZopjd4/9cagGPiux3e4Gl1tcpwDF27y9vdH+ftqIg83rMC7PQKpXLro1zwsTrLnXE83p/NDzx90WUMyN9KPWwgbc3NyY3yr8VxIuMDSI0ttdpyQ6r6sH3k/b3auz/bTMXT6eBtL/ziHyVywXi0l2RfHviAiPoK3W75tN6F9NxLcQhSSNpXb0KVmFz478hnn4s/Z7DjORgMvhtZi86vtaV7Tlynrj9Pr050cvhRns2MWV5GJkSw6vIhO1TvRzr+d3uXcMwluIQrRG83fwM3JjSm7p9h85feqvh4sG9CcT55qytWEdHp9spN3fzpGYlqmTY9bXNy28G9z/Rb+tYYEtxCFqKx7WUY3Hc2+6H2si1hn8+MpikLXxpXYOrY9z7SqzvJd5+n48TZ+PXrF5m8cjm7ThU3sjNIW/q3gWUHvcvJFgluIQtanbh+CywUzc99M4tLiiuSY3m7OTO7ZiB9eaouvpyvDvzrI4OX7uRSbUiTHdzSJGYlM3zudBr4N6Fevn97l5JsEtxCFzKAYmNh6IokZiXx84OMiPXaTqqVZN6It47s24M+zN+j08XYWbz9Lpty8vM38v+ZzPfU6k1pP0n3hX2tIcAthA3XL1OW5wOf44cwP7I8u2u6wTkYDgx8IYMvY9rSt7cf7v5yk+//+4ODF2CKtw14du36Mr09+Tb/6/Qgsq//Cv9aQ4BbCRoYHD6dKqSpM3j2ZDHNGkR+/Sml3ljzXjIXPhBCXksnjC/5k/NojxKeW3JuXZouZ93a9Z1cL/1pDglsIG3F3cuedlu9wLv4cy44u06UGRVHo3KgiW8a2Z0CbGqzcc5GOH29jXfjlEnnzctXfqzhx8wRvtHgDLxcvvcuxmgS3EDb0gP8DPFz9YRYfXszFhIu61VHK1YlJ3QP58eX7qejtxitf/8WAZfu4eKPk3LzMWfi3clseqf6I3uUUiAS3EDb2Zos3cTG6FEnf7rsJ8vdh7cttmdS9IfvP36TT7G18GnamRNy8nLFvhl0u/GsNCW4hbKy8R3lGNh3J7iu7+eXcL3qXg9GgMLBtTbaMbU+HeuWZ8evfdJ23g/3nb+pdms3suLSDTRc2MbTxUKp629fCv9aQ4BaiCDxZ90mCygYxY98M4tPtY17tSj7uLHw2hM+ea0Zyupk+C3cx7vvDxKUU/Y1UW0o1pTJtzzRq+tRkQOAAvcspFBLcQhQBo8HIxNYTiU+PZ87BOXqXc5uODSuw6dV2DG0XwDf7L/HQrG2s/StK98s6hWXx4cU5C/+6GF30LqdQSHALUUTq+9bn6QZPs+bUGg5dO6R3ObfxdHXi7UcbsG7E/VT19WD06kM8u3Qv564n611agZyJPcMXR7+gR60eNK/YXO9yCo0EtxBF6OUmL1PRsyLv7XqPTIv99aduWNmb715sw5SegYRHxvHInO3M23qadJNZ79LyTVVVh1j41xoS3EIUIQ9nD95u8TZn4s6w4tgKvcvJldGg8GzrGmwd255ODSvw8eZTPDp3B7sjCmdNzaKy9sxaDl47yJiQMfi6+epdTqGSFXCE0MHo30ezM2onP/T8AX8vf73LuaOwv68x4cejRN5MxcPFmPXHCQ8XI+4uRjxdnLL+NuKe9fitX+ds72rEw/mWr2/Zj7OxcNuQsWmx9FjbgwCfAJZ1Xmb3a0jm5k4r4EhwC6GD6ORoeq7tyX0V7mPBQwvsvl9xaoaZr/de5Ep8KskZZlIzzCSnm0jNNJNyy9fJ6WZSM0ykZJrJT7Q4G5WcEM8O9Ow3g1sfd3dxynpT0B73dDXi7mzE09Up6zHtjWTe4alsvriBb7qtpq5vXdudGBu6U3A7FXUxQgio6FmRV+57hen7prPxwkY61+isd0l35O5iZND9Ne95e1VVScu0kJJhIiXDnPUnj6/TtaBPSc967JavrydlkJKRor1RZL1hZNxlsJDRPQKPGutIv96eRz46jbtzxL/eFG5/M/Bwdcr6JJD1tcvtbwaeuXy6sMWnhPyQ4BZCJ/3r9+ensz8xfe902lZu69BzZ/yboii4Z4WdXyHvO9NsISW71Z9hymn9p2SaSUxLY/aJT8g0l+fJ+17CZDJqnwgytE8C2eGfkmHielL6f95E8sPFaLjlElFW0Dvf/mbwUIPydG5UqZDPgAS3ELoxGoxMajOJp35+inkH5/FOq3f0LskhOBsN+Lgb8HF3/s9znx35jOvpkXzy0Ce082+Yr/1aLCpppuxPAWZSMk3/fH2XTw7ZbyIpGWZiktJJual9SqhR1rOwvu3bSHALoaNAv0D61+/PyhMr6VGrB0HlgvQuyWFFJkayMHyh1Qv/GgzZ19mdoJQNCixEjnerVYhiZkSTEZTzKMd7u97DZDHpXY5DUlWV9/e8j1Ex8kbzN/Qux+YkuIXQWSmXUoxrMY6/Y//m/078n97lOKTNFzbzR9QfvHLfK1T0rKh3OTYnwS2EHXio2kOE+ofyyaFPuJx0We9yHEpSRtI/C//Wd7yFf60hwS2EHVAUhXEtxwHwwZ4Pis0ET0Vh/qH5xKTGMKHVBJwMJeO2nQS3EHaicqnKvBT8EmGXwvjt4m96l+MQjt3QFv7tW69vibqxK8EthB15uuHT1CtTj/f3vk9ypmPPzGdrZouZybsm4+vmy8imI/Uup0hJcAthR5wNzkxsPZGYlBjm/zVf73Ls2uq/V3P8xnHebP5msRq8dC8kuIWwM43LNebJek+y8uRKjt04pnc5dulayjXm/TWPNpXb8EgNx1741xoS3ELYoVFNR+Hr5svkXZMxWxxvLmxbm7FvBpnmTMa3HG/3E3TZgtXBrSjKUkVRdimKMr4wCxJCgJeLF2+2eJPjN46z6u9VepdjV/6I+oON5zcWm4V/rWFVcCuK0hswqqraGghQFKVO4ZYlhHik+iO0rdKWeQfnEZ0crXc5diHNlMbU3VOp4V2DgY0G6l2ObqxtcYcC32R9vQm4/9YnFUUZqijKfkVR9sfExBSgPCFKLkVRGN9yPBbVwvS90/Uuxy5kL/w7sfXEYrPwrzWsDW5PICrr65tAhVufVFV1saqqzVRVbVauXLmC1CdEiebv5c+w4GFsubiFsMgwvcvR1dm4syw7tqzYLfxrDWuDOwlwz/q6VAH2I4S4i+cDn6d26dq8v+d9UjJT9C5HF9kL/3o4eRS7hX+tYW3gHuCfyyPBwPlCqUYI8R/OBmcmtZ7EleQrfHroU73L0cWPZ3/kwNUDxXLhX2tYG9xrgWcVRfkYeBL4udAqEkL8R5PyTehTtw9fnfiKkzdP6l1OkYpLi2PW/lncV/4+HqvzmN7l2AWrgltV1QS0G5S7gQ6qqsYXZlFCiP8a3XQ0Pq4+Ja5v98cHPiYpI4nxrcY75GrttmD1WVBVNVZV1W9UVZV+SkIUAR9XH15v/jpHrh/h21Pf6l1OkThw9QA/nPmBZwOfpW4Zx1yt3Rbk7UsIB9K1ZldaVWrF3INzuZZyTe9ybCrTnMmUXVOo7FmZ4Y2H612OXZHgFsKBKIrChFYTyDBnMGPfDL3Lsanlx5dzNv4sb7d8Gw9nD73LsSsS3EI4mGre1RjaeCgbz29kx6UdepdjE5cSL7EofBEdq3WkfdX2epdjdyS4hXBAAxsNpKZPTabtmUaqKVXvcgpV9sK/BsXAmy3e1LscuyTBLYQDcjG6MLHVRKKSolgUvkjvcgrVlotb2BG1g5ebvFwiFv61hgS3EA6qWcVm9Krdi+XHlnM69rTe5RSKpIwkPtzzIfV96/NUg6f0LsduSXAL4cDGhozFy8WLybsmY1EtepdTYJ8c+oSY1BgmtppYYhb+tYYEtxAOrLRbacY2G8uhmEN8d/o7vcspkOM3jrPy5EqerPdkiVr41xoS3EI4uOzZ8mYfmM311Ot6l2OVkrzwrzUkuIVwcNl9u9NMaXy07yO9y7HKN6e+4diNY7zR/A28Xbz1LsfuSXALUQzU9KnJ4KDB/HLuF/68/Kfe5eTLtZRrzDs4j9aVWtO5Rme9y3EIEtxCFBMvBL1Ade/qTN09lTRTmt7l3LOP9n1EhjmD8a1K5sK/1pDgFqKYcDW6MqHVBCITI1lyZIne5dyTnVE7+fX8rwxpPIRq3tX0LsdhSHALUYy0rNSS7gHd+fzo50TERehdzh3duvDvoEaD9C7HoUhwC1HMjG02Fg8nDybvnoyqqnqXk6fFhxdzKekSE1pNKNEL/1pDgluIYsbP3Y+xzcZy4OoB1p5Zq3c5uYqIi2DZsWV0D+hOi0ot9C7H4UhwC1EM9ardi6blmzLrwCxupt3Uu5zbyMK/BSfBLUQxZFAMTGw9keTMZGbtn6V3Obf56exP7L+6n1dDXsXP3U/vchySBLcQxVSt0rUYGDiQn87+xN4re/UuB/hn4d8m5ZrQu05vvctxWBLcQhRjQxsPxb+UP1N2TyHDnKF3Ocw+OJuEjAQmtJ4gC/8WgJw5IYoxNyc3JrSawPmE8yw9slTXWg5ePcj3p7/nuYbPycK/BSTBLUQx16ZKG7rU7MKSI0s4H39elxoyzZlM2T2FSp6VGB4sC/8WlAS3ECXAG83fwM3oxtTdU3Xp273i+ArOxJ2RhX8LiQS3ECVAWfeyjA4ZzZ7oPayPWF+kx45KimJh+EIerPogoVVDi/TYxZUEtxAlRJ+6fQguF8xH+z4iLi2uSI6ZvfCvoiiMazmuSI5ZEkhwC1FCZPftTsxIZPbB2UVyzK0Xt7L90nZZ+LeQSXALUYLULVOXZwOf5fvT33Pg6gGbHis5M5kP9n5AvTL1eLrB0zY9VkkjwS1ECTO88XAqe1Zm8q7JZJozbXac+X/NJyYlhomtZeHfwibBLUQJ4+HswTut3iEiPoIvjn1hk2PcuvBv43KNbXKMkkyCW4gSqJ1/OzpV78Siw4uITIgs1H2bLWam7JpCadfSsvCvjUhwC1FCvdXiLZwMTkzdU7h9u7899S1HbxyVhX9tSIJbiBKqvEd5Rt43kj8v/8mGcxsKZZ8xKTHMPTiXVpVa8WjNRwtln+K/JLiFKMH61utLI79GzNg3g/j0+ALvTxb+LRoS3EKUYEaDkYmtJxKbHsvcg3MLtK8/o/5kw/kNDG48mOre1QupQpEbCW4hSrgGfg14psEzfHvqWw5dO2TVPtJMaUzdoy38+0KjFwq3QPEfEtxCiJyRjZN3TybTkv++3UuOLCEyMZLxrcbLwr9FQIJbCIGHswdvt3ib07Gn+fL4l/l6bUR8BJ8f/ZxuAd1oWamljSoUt5LgFkIA0KFaBx6s+iALDi0gKinqnl6jqipTd0/F3cldFv4tQvkObkVRKiiKssMWxQgh9DWu5TgMioFpu6fdU9/udRHr2Be9j1dDXqWse9kiqFBAPoNbUZQywHLA0zblCCH0VNGzIiPuG8GOqB1svrD5jtvGp8czc99MgssF83idx4uoQgH5b3Gbgb5Agg1qEULYgf71+9PAtwEf7v2QxIzEPLebfSBr4d9WsvBvUbvj2VYUZZGiKGHZf4DRqqretZe+oihDFUXZryjK/piYmMKqVQhRBJwMTkxqPYkbaTf431//y3Wbv679xXenv+PZhs9Sz7deEVco7jjXoqqqw6zZqaqqi4HFAM2aNSv6Be6EEAUSWDaQfvX68fXJr+ke0J2gckE5z2VaMpm8azIVPSvyYvCLOlZZcsnnGyFErl657xXKuZdj8u7JmCymnMe/PP6ltvBvC1n4Vy8S3EKIXJVyKcVbLd/i5M2TrDyxEtAW/l1waAEdqnagQ7UOOldYclkV3KqqhhZyHUIIO9SxWkfa+7dn/qH5XEm6wgd7PtAW/m0hC//qSVrcQog8KYrC2y3fBmDo5qFsu7SNl5u8TKVSlXSurGST4BZC3FHlUpV5Kfglziecl4V/7YSs4CmEuKunGz5NUmYSXWp2kYV/7YD8BIQQd+VscGbEfSP0LkNkkUslQgjhYCS4hRDCwUhwCyGEg5HgFkIIByPBLYQQDkaCWwghHIwEtxBCOBgJbiGEcDDKvawrV6ADKEoMcMHKl5cFrhdiOSWBnLP8kfOVP3K+8qcg56u6qqrlcnvC5sFdEIqi7FdVtZnedTgSOWf5I+crf+R85Y+tzpdcKhFCCAcjwS2EEA7G3oN7sd4FOCA5Z/kj5yt/5Hzlj03Ol11f4xZCCPFf9t7iFkII8S8S3EII4WB0C25FUXwURdmgKMomRVF+UBTFRVGUpYqi7FIUZfwt21VQFGXHLf9+UVGUsKw/hxRFWaTPd1C0CnC+yiiK8ouiKPtLyrmCAp2vmoqi/Kwoyg5FUWbpU33Ru5fzlds2WY//57wWdwU8X7f9zllDzxb308DHqqo+DEQD/QCjqqqtgQBFUeooilIGWA54Zr9IVdUFqqqGZq00vwNYUvSl68Kq8wU8C/xfVl9SL0VRSkofXGvP13RgiqqqDwD+iqKEFm3Zurnr+cplm86KovTOZbuSwNrzldvvXL7pFtyqqn6qqurmrH+WA54Bvsn69ybgfsAM9AUS/v16RVGqABVUVd1fBOXqrgDn6wbQSFGU0kBVILJICtZZAc5XXeBg1tfXAB/bV6u/ezlfuWxzDQj993ZFU7G+CnC+8sy0/ND9GreiKK2BMmiBEpX18E20UE5QVTU+j5e+DCwoghLtihXn6w+gOjASOJG1bYlhxflaA0xSFKU70BnYWmTF2oE7na9/b6Oq6m60lmOu25UE+T1fd8m0e6ZrcCuK4gv8DxgEJAHuWU+V4g61KYpiADoAYTYu0a5Yeb4mAcNVVZ0MnAQG2rpOe2HN+VJVdSqwARgMLFdVNakISrUL93K+/rUNeW1XElh5vgqFnjcnXYBvgXGqql4ADvDPx6xg4PwdXv4AsEctQZ3QC3C+ygBBiqIYgZZAiThnBfz9OgRUAz62YYl25V7OVy7bkNt2RVa0jgpwvgqHqqq6/AFeBGLRWs1hwPNAONp/lhOAzy3bhv3rte8DvfWq3ZHOF9ACOIbWItgMlNL7e7Hn85X17/eAZ/X+HuztfOWyTV/AO6/zWpz/WHu+8vqdy+8fuxo5mXXHtROwXVXVaL3rsXdyvvJHzlf+3Ov5kvOqKcrzYFfBLYQQ4u5KzI0EIYQoLiS4hRDCwUhwCyGEg5HgFkIIByPBLYQQDub/AVy5YA+YEHw4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_revenue_pic(stock_df, '图表保存测试/1.jpg')\n",
    "save_net_income_pic(stock_df, '图表保存测试/2.jpg')\n",
    "save_profit_rate_pic(stock_df, '图表保存测试/3.jpg')\n",
    "save_cost_rate_pic(stock_df, '图表保存测试/4.jpg')\n",
    "save_dupont1_pic(stock_df, '图表保存测试/5.jpg')\n",
    "save_dupont2_pic(stock_df, '图表保存测试/6.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c68ba5e2-b931-4626-afae-4ec71b6cfa7a",
   "metadata": {},
   "source": [
    "## 文字图表合并"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f18a8ef-a16d-46da-ac6a-37c493ca6c6d",
   "metadata": {},
   "source": [
    "### 先保存好图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "21508bc4-6080-4bdf-b577-c50f065546b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "root_path = '年报图表'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "78799872-8820-4dde-946f-81730026e50c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "stock_doc_path = f'{stock_name}-{stock_id}-{report_year}'\n",
    "pic_path = os.path.join(root_path, stock_doc_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "44307b56-1333-4c62-af11-be340d8fcba4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(pic_path):\n",
    "    os.makedirs(pic_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "6d62e418-757d-488d-8847-d8b0f44a8703",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_revenue_pic(stock_df, os.path.join(pic_path, '1.jpg'))\n",
    "save_net_income_pic(stock_df, os.path.join(pic_path, '2.jpg'))\n",
    "save_profit_rate_pic(stock_df, os.path.join(pic_path, '3.jpg'))\n",
    "save_cost_rate_pic(stock_df, os.path.join(pic_path, '4.jpg'))\n",
    "save_dupont1_pic(stock_df, os.path.join(pic_path, '5.jpg'))\n",
    "save_dupont2_pic(stock_df, os.path.join(pic_path, '6.jpg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "482bdc84-1477-40b8-aab7-6a459de9a7b8",
   "metadata": {},
   "source": [
    "问题：同时添加2个图片到1行怎么办？\n",
    "https://stackoverflow.com/questions/57361943/add-two-images-in-same-line-in-python-docx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60cec3be-d525-40c5-a89c-a182659c513d",
   "metadata": {},
   "source": [
    "自己用word尝试了下，发现7.5cm宽度正合适，厘米英寸转换麻烦，自然想到导包中有厘米单位，from docx.shared import Cm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b31890e-4604-4de1-a0f9-50fd38d43f86",
   "metadata": {},
   "source": [
    "### 再导出结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "1c60500d-a3fb-4296-8e09-cfae2a809096",
   "metadata": {},
   "outputs": [],
   "source": [
    "from docx import Document\n",
    "from docx.shared import Cm\n",
    "from docx.oxml.ns import qn\n",
    "from docx.shared import Pt \n",
    "\n",
    "document = Document()\n",
    "\n",
    "# 修改字体\n",
    "document.styles['Normal'].font.name = 'Times New Roman'\n",
    "document.styles['Normal']._element.rPr.rFonts.set(qn('w:eastAsia'), u'微软雅黑')\n",
    "\n",
    "# 标题部分\n",
    "heading = document.add_heading(f'{stock_name}({stock_id}){report_year}年报分析', 0)\n",
    "change_font(heading)\n",
    "\n",
    "# 第一段文字\n",
    "heading1 = document.add_heading('营收利润情况', level=1)\n",
    "change_font(heading1)\n",
    "p1_text = stock_revenue_status(stock_name, stock_id, stock_df)\n",
    "document.add_paragraph(p1_text)\n",
    "# 第一组图片\n",
    "para_pic1 = document.add_paragraph()\n",
    "para_pic1.add_run().add_picture(os.path.join(pic_path, '1.jpg'), width=Cm(7.5))\n",
    "para_pic1.add_run().add_picture(os.path.join(pic_path, '2.jpg'), width=Cm(7.5))\n",
    "\n",
    "# 第二段文字\n",
    "heading2 = document.add_heading('运营效率情况', level=1)\n",
    "change_font(heading2)\n",
    "p2_text = stock_efficiency_status(stock_name, stock_id, stock_df)\n",
    "document.add_paragraph(p2_text)\n",
    "# 第二组图片\n",
    "para_pic1 = document.add_paragraph()\n",
    "para_pic1.add_run().add_picture(os.path.join(pic_path, '3.jpg'), width=Cm(7.5))\n",
    "para_pic1.add_run().add_picture(os.path.join(pic_path, '4.jpg'), width=Cm(7.5))\n",
    "\n",
    "# 第三段文字\n",
    "heading3 = document.add_heading('杜邦分析', level=1)\n",
    "change_font(heading3)\n",
    "p3_text = stock_finance_status(stock_name, stock_id, stock_df)\n",
    "document.add_paragraph(p3_text)\n",
    "# 第三组图片\n",
    "para_pic1 = document.add_paragraph()\n",
    "para_pic1.add_run().add_picture(os.path.join(pic_path, '5.jpg'), width=Cm(7.5))\n",
    "para_pic1.add_run().add_picture(os.path.join(pic_path, '6.jpg'), width=Cm(7.5))\n",
    "\n",
    "# 第四段文字\n",
    "p4_text = stock_industry_status(stock_name, stock_id, industry_df, rank_index_list, index_direction_list, tmp_rank)\n",
    "if p4_text:  # 字符串不为''就是True\n",
    "    heading4 = document.add_heading('行业情况', level=1)\n",
    "    change_font(heading4)\n",
    "    document.add_paragraph(p4_text)\n",
    "\n",
    "# 翻页获取表格\n",
    "# document.add_page_break()\n",
    "finance_table = get_finance_table(stock_df)\n",
    "dupont_table = get_dupont_table(stock_df)\n",
    "document = add_df_document(finance_table, document, '财务摘要（百万元）')\n",
    "document.add_paragraph('\\n')\n",
    "document = add_df_document(dupont_table, document, '杜邦分析指标')\n",
    "\n",
    "# 保存\n",
    "document.save(stock_doc_path+ '年报分析.docx')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c848fa8-6f3b-4288-a990-4bfb24b98f35",
   "metadata": {},
   "source": [
    "# 最终函数封装"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e5fe3bc-bf91-4217-9011-05f96611ec1b",
   "metadata": {},
   "source": [
    "## 测试函数参数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cbc637e-6be7-4893-ac0f-f2cd36aa2d56",
   "metadata": {},
   "source": [
    "【国君化妆品】上海家化2021年报点评：护肤品类驱动增长，毛利提升优化盈利：https://mp.weixin.qq.com/s/cmcj2vIZ9iI8dvwl855pyQ  \n",
    "【国君食品|年报点评】海天味业：短期承压，长期乐观：  \n",
    "https://mp.weixin.qq.com/s/xHlJyIoyDaNgY_zZF1THcw"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "573971ec-cd17-40a2-a1d6-3d1a67e57282",
   "metadata": {},
   "source": [
    "上海家化[600315]  海天味业[603288]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89a21631-9972-4b68-a3a1-81931465072d",
   "metadata": {},
   "source": [
    "## 重启内核重试"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcb368aa-e7a5-4a07-a613-5a4c596757bc",
   "metadata": {},
   "source": [
    "这样才能真正的清除缓存的干扰，从0到1测试好结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "e1db96cf-2bd5-4502-8e97-81c12496c0e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "total_df = pd.read_csv('2023-01-08 加工导出.csv', index_col=['公告日期', '股票代码'])"
   ]
  },
  {
   "cell_type": "raw",
   "id": "8b79a275-4ddf-4d83-9b3a-92990b5e4e43",
   "metadata": {},
   "source": [
    "# EBIT没对上，不过也就这样吧\n",
    "total_df.swaplevel().loc['600315.SH'].T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bcb6fe6-3326-4d5b-896e-c97822f4643f",
   "metadata": {},
   "source": [
    "- 漏复制代码：中文乱码代码，industry_df，tmp_rank\n",
    "- 函数内变量错误：sale_rate_flag/sale_rate未替换，表格转文字的pd没导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "54d9b2b3-7638-4a94-8498-8851d8499856",
   "metadata": {},
   "outputs": [],
   "source": [
    "from afan_docx import output_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "2bf9fecc-a91f-4608-a049-9e3e22dc3635",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_report('603288.SH', '2021', total_df)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "99ad7a89-fa95-4aa5-9989-ee174a9df787",
   "metadata": {},
   "source": [
    "# 图都没画\n",
    "from afan_docx import output_report\n",
    "output_report('603288.SH', 2021, total_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5235992a-d2d9-409b-8719-fb92e6c472a1",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 后续计划"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60fc2c41-312c-4c85-a951-cece960ac630",
   "metadata": {},
   "source": [
    "- 1 报告文字更加精细，如接近下输出“持平”、金额单位根据实际情况输出“万”、“亿”\n",
    "- 2 报告分析语句更加丰富，考虑近几年的趋势和历史高点，以及子项贡献\n",
    "- 3 标题太平淡了，但这个得借助NLP了\n",
    "- 4 部分指标如EBIT还是没算对"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6866ce6-7020-47b5-a9e2-2c5bb3c4a3d2",
   "metadata": {},
   "source": [
    "强调：\n",
    "- 1 AFAN写的时候也在不断调试格式，所以大家感觉自己写的不好不要灰心，我这花了很长时间\n",
    "- 2 想做好一个自动化报告系统其实很复杂\n",
    "- 3 指标的确认也很重要，不然是无用功"
   ]
  }
 ],
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